Data Science Interview Masterclass

What you’ll learn

• A clear explanation of how analytics case interviews actually work, with an emphasis on turning ambiguous business questions into coherent, end to end analytical approaches

• Guidance on using simple, flexible frameworks and verbalizing judgment calls as the conversation unfolds, rather than forcing a memorized structure

• An overview of common case patterns such as new product launches and zero to one problems, focusing on how experienced analysts choose early success metrics and design pragmatic experiments when data is limited

What you’ll learn

• Breakdown of a time-based framework for case interviews: 15% intro (clarifying context), 15% set the stage (defining success metrics, presenting roadmap), 30% problem space, 30% solution space, 10% synthesis/wrap-up

• An emphasis on clarifying context upfront, defining success metrics and guardrails early, and laying out a clear analytical roadmap before diving into details

• A systematic approach to problem solving using hypotheses across product, business, and technical considerations, with solutions explicitly tied back to the original success criteria.

What you’ll learn

• Core principles that consistently strengthen case interview answers, starting with clearly defining the problem space before moving to solutions and grounding recommendations in the company’s actual strategy, not buzzwords

• Guidance on using simple structures, such as the rule of three, to cover the problem space without sacrificing depth, while making tradeoffs and collaboration across functions explicit

• An emphasis on avoiding generic metrics and instead presenting a holistic business perspective alongside sound analytical reasoning, a distinction interviewers increasingly expect at senior levels

What you’ll learn

• A practical evaluation lens that mirrors how interviewers assess cases in practice, covering problem definition, upfront structure, hypothesis driven exploration, solution prioritization using effort versus impact, and clear synthesis with next steps

• A detailed comparison of weak versus strong responses at each stage, highlighting common failure modes such as jumping straight to solutions and failing to maintain alignment with cross functional partners as the analysis unfolds

Prompt
In the past we thought that more connections in one’s network would lead to more use of our company’s social network product, but then we started to notice this is not necessarily the case. How would you test this assumption?

Adequate Answer

Intro
Got it, so it sounds like in the past encouraging people to connect more led to positive engagement on the platform, though this may have changed recently. I can certainly see how this has strategic relevance for the company. Before I jump into some of my ideas, I’d like to ask a few clarifying questions:

• First: do we know if robust statistical methods were used to draw the conclusion that more connections directly cause less engagement (e.g. RCTs)? [wait for interviewer’s response]

• Second: given the phrasing “started to notice a change,” has this change occurred gradually over time or was this sudden? And do we have a sense of whether this impacts most users or is it limited to specific populations? [wait for interviewer’s response]

• Third: Do we have a clear definition for “engagement” or is that something I can define in my answer? [wait for interviewer’s response]

Ok, thanks for the additional context. Could I have a moment to gather my thoughts? [wait for interviewer’s response]

Set the Stage
First, I think it’s extremely important to anchor any major actions on company mission. I know the mission of company XYZ is to enable the best social experiences in the world, and, naturally, connections on XYZ’s platform help fuel these experiences.

Second, I would suggest that we define success metrics as it relates to this case, as it would help make the investigation more objective and support subsequent measurement of interventions we may enact: we can quantify our measure of engagement as MAU on the platform. Does this sound good to you? [wait for interviewer’s response]

Third, it’s essential that we dig into a key analytics theme at play here, which is the aspect of correlation vs causation. This is fundamental to the case because engagement on the platform is driven by a variety of factors, where number of connections is just one among many. In order to draw a causal link between number of connections and engagement, we need reliable empirical methods. Simple correlations alone or even post facto regression techniques may not be sufficient.

Ok, with this said, I’d like to propose a roadmap for the case at hand:

• First, I’ll clarify the problem space, covering what we can learn from past initiatives, hypotheses, as well as data sources and analytics methodologies that might assist us in validating or invalidating these hypotheses

• Next, I’ll jump into my ideas for the solution space. Once we have a clear sense of the potential problems and opportunities, we can consider what actions we might take to improve the user experience as it relates to connections and engagement on the platform.

• Lastly, I’ll cover next steps: how we might operationalize these ideas

Does this sound good to you? If so, I’ll jump into each of these sections [wait for interviewer’s response]

Problem Space
Ok, as we unravel the problem space, it’s important that we consider the potential range of possibilities that might explain the observed trend between user connections and overall engagement. I’ll share a few hypotheses as a starting point.

- Causation:

• As one’s network of friends increases, a user might feel inhibited to share via broad public posts. What felt comfortable to share for a few dozen close connections feels inadequate for several hundred or thousand connections viewing one’s posts.

• As one’s network of friends becomes more varied, posts that were generally relevant before are not as targeted, so users become disengaged because of this lower relevance of content.

• A significant portion of connection requests are fake, which have discouraged users from using XYZ’s platform due to “social connection pollution.”

- Correlation:

• Users with fewer connections also happen to prioritize quality of engagement on the platform, but it’s not that having fewer connections causes these users to engage more. In this scenario, the relationship between number of connections and engagement is just spurious.

• Our expectations don’t align with reality: as average tenure on the platform increases, user engagement proportionally decays, whereas we expected linear growth between number of connections and engagement. Deepening into the analysis might reveal that engagement naturally follows an S-curve trajectory and more tenured users are simply further along on that trajectory (and also happen to have more connections since that’s expected for more tenured users)

• User preferences have changed; over time, users have gravitated to private messaging and other forms of interactions that may not be captured in the success metric. In this scenario, we have more of a measurement challenge than a real disengagement problem.

Would you like me to elaborate on any of these? Otherwise I’ll move on to the next sections. [wait for interviewer’s response]

Once we have hypotheses formed, the next step would be to use analytics methodologies and data to validate or invalidate these. Preemptive analysis could inform whether there’s been an increase in fake connection requests or a mix-shift in engagement across the platform’s features, for example. But more broadly, we would want to use experimentation to identify objectively what causes user engagement to change. In terms of methodologies, naturally the ideal would be to use randomized controlled trials, the gold standard for drawing causal conclusions. Given natural constraints in experimenting with a variable such as “number of connections”, though, we would have to change our approach and go further down the ladder of empirical evidence, so to speak. Here are a few different options available:

• Qualitative data: interview users via in-app surveys to understand the trend and sentiment regarding engagement vs friend count; naturally this doesn’t scale well but it might shed some color on underlying motivations

• Quasi-experimental causal inference techniques such as matching and propensity scores: We can account for important attributes that would otherwise confound the relationship between engagement and number of connections such as demographics, engagement, region, device, type of product usage, interests. This allows us to work with historical data instead of having to launch an A/B test anew; we’d have to define the synthetic test/control groups based on prior knowledge and subject-matter expertise.

• Randomized A/B test with nudging: A/B tests would only be feasible in the context of manipulating new features (that encourage / discourage friend requests; e.g. nudge users to connect). While we can’t have a holdout group of users who aren’t allowed to add friends / accept requests, we can make it easier or more difficult to do so and draw conclusions accordingly; the randomization aspect is ideal, but we end up answering a more narrow question than what the case asks for, and we may be limited to a specific subpopulation such as new users

Would you like me to deep dive into any of these in particular? [wait for interviewer’s response]

Solution Space
Ok, so assuming we have a reasonable sense of the issue at hand and root cause, we could proceed with what we can do in light of what we have unearthed in the problem space. Naturally, any actions we take need to be firmly rooted in the underlying business context. But I’ll proceed given the limited information available. If the relationship between engagement on the platform and number of connections is not in fact causal, we have limited options for effecting change, but we can still improve our reporting and metrics used. For example, updating success KPIs for our measure of “engagement health” to reflect more recent engagement patterns on the platform. Here, though, I’ll assume that we have identified that, in fact, having too many connections does in fact lead to lower engagement, if that works for you. [wait for interviewer’s response]

The next step would, naturally, be to take appropriate actions, such as using scalable approaches to counter fake connection requests or to recommend higher quality connections to users, potentially by running a predictive algorithm to infer future interactions among prospective users whom we might recommend as connections. We could also explore in-product features to nudge users to find new ways of engaging with existing connections; we have several levers at our disposal such as feed ranking algorithm and notifications to surface content from quality connections, as well as new features such as auto-generating thematic messaging threads based on mutual interests. I’d be happy to elaborate on any of these if you’d like. [wait for interviewer’s response]

Synthesis & Next Steps
These are my main ideas. In summary, I would take time to understand in depth prior learnings and the overall business context. I would then form hypotheses to capture the range of possible explanations for the observed trend. I would then use data to validate or invalidate the hypotheses. Once we have a clear sense of what is driving the trend, I would then work closely with the PM on actions we can take to align feature development to nudge users towards healthy engagement on the platform.

What you’ll learn

• Behavioral interviews assess the "how" beyond technical "what" with different contexts: Big Tech evaluating culture fit and thriving potential versus startups seeking force multipliers who create leverage, empower teams, and free up hiring manager time

• Core framework requiring authentic consistency: select 3-4 identity themes (technical depth, curiosity, stakeholder-first collaboration, detail-oriented, executive-focused) and reinforce them across all interview panels to enable hiring managers to build a powerful narrative during debrief discussions

• Story bank strategy using 5-6 flexible examples that highlight your chosen dimensions repeatedly, contextualizing for specific interview focus (people leadership, technical depth) while maintaining overarching consistency from intro through closing

• Authenticity over overfitting: if role requirements conflict with your core strengths (e.g., coaching-heavy culture when you're impact-driven), reconsider fit rather than force misalignment since follow-up questions will expose unnatural positioning and prevent you from being in your element

What you’ll learn

• Pick two or three clear angles that genuinely reflect how you operate, not a scattered “I do everything” posture, fewer angles are easier for you to sustain, easier for interviewers to remember, and harder to poke holes in during follow ups

• Use those angles deliberately across the loop so they repeat, debriefs reward consistency, if multiple interviewers independently report the same strength, it carries far more weight than a dozen different ones

• Adapt how you express each angle based on the interview context while keeping the core intact, it helps to name your strengths directly in answers so interviewers don’t have to infer them

• Treat “tell me about yourself” as your opening argument, not a resume walkthrough, lead with what you’re about and how you create impact, then flow naturally into why that maps cleanly to the role you’re interviewing for

What you’ll learn

• Use stories to prove how you think, not just what you did, anchor every example in a clear takeaway so interviewers leave with a lesson they can reuse, not a timeline they forget

• Upgrade STAR by centering decisions, not actions, and by explaining the tension that made the choice non-obvious, this is what separates execution from judgment

• Prepare a small set of stories you know cold, each with a tight internal spine, so you can adapt in real time without sounding scripted or scrambling for examples

• Make your signal explicit, state the principle behind each story so interviewers don’t have to infer what you’re good at, this is how strong narratives survive debriefs

What you’ll learn

• The core probability ideas you actually need in interviews, how to add probabilities without double counting, when multiplication applies, and how to reason about conditional probability, with emphasis on knowing when independence assumptions break down in real user behavior

• A practical explanation of Bayes’ rule as a way to update beliefs with new evidence, using the classic medical test example to show why ignoring base rates leads to wildly wrong conclusions

• How Bayes shows up in real systems like spam filtering, where prior rates and observed signals are combined to estimate likelihood rather than relying on any single indicator

• An interview-first approach that prioritizes clear reasoning over formulas, by explicitly defining events, stating what you’re conditioning on, and walking through the logic as if you were explaining it to a teammate

• Higher-level reasoning traps interviewers like to test, such as interpreting multiple A/B test results under known false positive rates, reinforcing the habit of asking “what’s the universe, what are the events, and what changes my belief” before touching math

What you’ll learn

• A mindset shift for interviews from memorizing formulas to showing how you reason with data, explain uncertainty, and justify metric choices in ways that support real business decisions

• A practical way to talk about central tendency, knowing when the mean tells the right story, when the median is more representative in skewed data, and when the mode matters for categorical analysis, with answers tied directly to what decision you’re trying to make

• How to reason about spread alongside averages, using standard deviation and IQR to understand consistency and risk, and recognizing that identical means can hide very different user experiences or product health

• Common mistakes interviewers listen for, like quoting averages without variability, applying the mean blindly to skewed data, or mixing up sample and population calculations without realizing the implication

• A clear explanation of the Central Limit Theorem that focuses on intuition over math, understanding that averages behave more predictably as samples grow, enabling confidence intervals and hypothesis tests even when raw data isn’t normally distributed

What you’ll learn

• How to reason about power without memorizing formulas, starting from business reality, what lift would actually matter, what baseline you trust, how much risk of false positives you can tolerate, and how noisy the metric is, with strong answers explaining assumptions instead of reciting sample sizes

• Choosing the right statistical test by first understanding the data, large sample binary metrics, continuous metrics with distribution checks, categorical comparisons with enough support, and non-parametric alternatives when data is skewed, always validating assumptions visually before committing

• Ways to make experiments more efficient without changing the product, using variance reduction techniques like stratification and covariate adjustment when you have strong predictors, and knowing when these methods help versus when they add complexity without benefit

• Experiment designs beyond basic A/Bs, including A/A tests to catch instrumentation issues, multi-arm tests with proper correction for multiple comparisons, intent-to-treat analysis to preserve randomization, and cluster-level randomization when user interactions cause spillover

• More advanced setups for real production constraints, such as sequential testing that allows early stopping without inflating false positives, and pre-post or difference-in-differences approaches when clean randomization isn’t possible but stable trends exist

What you’ll learn

• A simple experimentation structure that mirrors how strong product teams think, starting with a clear goal and hypothesis, defining success and safety metrics upfront, setting clean eligibility and randomization, sizing the experiment before launch, and committing to decision rules in advance

• Turning vague goals into testable hypotheses by tying a specific change to a predicted user behavior and a directional outcome, avoiding unfocused redesigns or experiments that can’t explain why results moved

• A practical metrics setup that separates outcomes from context and safety, using a primary metric to make the call, secondary metrics to explain the movement, sanity checks to confirm the test actually ran, and guardrails to prevent quiet damage

• Setup fundamentals that preserve validity, restricting experiments to the right users, keeping randomization sticky, logging exposure and behavior cleanly, changing one thing at a time, and filtering out internal or test traffic that skews results

• Discipline around power and decision making, estimating baselines before launch, avoiding early peeking, recognizing when “no result” simply means underpowered, and pre-committing to thresholds so decisions don’t drift after seeing the data

What you’ll learn

• A concise entry point to machine learning case interviews, designed to give time constrained candidates a clear mental model before they go deep

• A practical breakdown of how ML cases are assessed in interviews, spanning data and feature choices, modeling decisions, evaluation tradeoffs, and the system design required to ship models into production

• Structured to work both as a fast reference and as a serious technical path through the ML interview lifecycle

What you’ll learn

• A core framework for ML interviews that emphasizes pre work and sequencing, starting from business context and objectives, moving through data understanding and modeling decisions, and ending with deployment, experimentation, and impact assessment

• Explicit guidance on integrating business tradeoffs into technical choices, including precision recall decisions tied to user experience and cost, model ROI considerations, responsible data use, and cross functional ownership across the lifecycle

• A disciplined approach to model selection that compares alternatives before converging, using ensembles and simplification principles where appropriate, and applying Occam’s Razor to avoid unnecessary complexity

• A practical view of deployment and productization, covering how models move beyond notebooks through MLOps workflows, retraining strategies, latency constraints, monitoring, and scale considerations

• A focused perspective on staying current in ML, emphasizing conceptual shifts and real world adoption patterns over buzzwords, with examples drawn from how leading product teams apply new techniques in production

Prompt
How would you scale a solution that detects fake news on Facebook?

Adequate Answer

Intro
I can definitely see how fake news detection would be an important use of ML in order to cultivate an environment where users can trust the content they see and engage socially in a way that aligns with the company’s community standards. I’ll walk through how I’d think through the problem and design an end-to-end solution.

Before I jump into some of my ideas, I’d like to ask a few clarifying questions:

• First: In terms of scope, can I assume that fake news could show up across various features of the app, including Feed, Groups, Watch and other parts of the app? [wait for interviewer’s response]dw

• Second: In terms of timeframe for implementing the solution, I would take a different approach if the goal was to prioritize speed vs building a more robust approach in the long-term. For example, parsing through fake news in text form would be easier than through other content types like images and video. Would you like me to highlight how my approach might change for a short-term vs long-term solution? [wait for interviewer’s response]

Ok, thanks for the additional context. Could I have a moment to gather my thoughts? [wait for interviewer’s response]

Set the Stage
First, we need to be clear about what we are trying to address. Fake news fits into the broader bucket of misinformation, which may be motivated by different use cases but ultimately results in swaying end-users towards a particular nefarious belief, action, or reaction. Needless to say, this is bad for Meta’s overall business as it hampers advertiser and end-user trust. Fake news can take several forms, ranging from instances in which visual or textual information is inserted into an article in order to subtly bias an argument, to wholly fabricated content, often including extraordinary claims created and shared systematically with the sole intention to deceive.

Ok with this context defined, I’d suggest diving into some of these specifically as they apply to the ML solution. For example, later I’ll refer to how the ML solution would be operationalized differently for text vs image-based content.

There are several things we need to accomplish and I plan on tackling this case in a few steps:

• In the problem space, so to speak, we would need to source the foundational data that we would use for ML models, along with feature engineering steps to enrich the input data.

• In the solution space, so to speak, I’d like to dive into the actual use of algorithms and how the solution would be built based on various sub-models

• Once we have a solution in mind, we can move to the operationalization phase, which is all about scaling the solution in a way that is harmonized with the broader tech stack and having a game plan for measurement, continuous retraining, and ongoing monitoring.

Does this sound good to you? If so, I’ll jump into each of these sections next. [wait for interviewer’s response]

Problem Space
Before jumping into technical details of the ML solution, I’d like to clearly define the problem space, which will include defining the building blocks of the end solution, focusing on data collection to prep for the modeling step. I would suggest collecting data by various dimensions in order to be comprehensive in terms of countering fake news. These would include the following:

• I’ll use ‘entity’ to describe the origination of the content on the Facebook platform. This would include users, of course, but also pages, groups, and any other source capable of originating content.

• Post origination source e.g. user vs page origination type, profile picture (copied from other account?), FB tenure, number of previous posts, number of friends (and rejection rate) etc

• Content details such as origination date and content type (eg text, photo, video)

• We could source data on length of post, inclusion of specific catch phrases (e.g. hot topic words like “vaccine”, “COVID”, “election” etc), inclusion of hyperlinks

• Content number of comments, reactions, user reported content (eg. people flagging a certain post or image and marking it as fake)

• Beyond the content itself, a key determinant of fake news might be the virality spread pattern via Facebook’s social graph. Perhaps fake news spreads faster and in a more unique pattern (eg less close friends engaging etc). I’d want to track impressions and reactions by minute, number of re-shares, and rate of impressions and engagement classified by distance in the network graph (eg primary connection, 2nd order connection etc). Naturally, I’d have to work closely with ML engineers who own the logic for content distribution, as this would factor into the spread pattern of fake news.

• I would also consider external datasets (eg Facebook’s "community notes") and fact-check websites we may be able to access to serve as ground truth for what news and sites are considered legitimate.

Once we have the basic data available, there are many ways that we could enrich the data. I would work in an iterative approach across data prep and modeling to ensure that we’re not over-optimizing in the data prep stage. That said, I do think that text mining the content effectively as a feature engineering step could ultimately make or break the model’s quality because we need to be able to capture topics and sentiment of posts.

Solution Space
Ok, now that we’ve understood the problem space and considered the building blocks for the end-solution, I’ll now jump into how I’d leverage ML at scale for fake news detection. I would envision a two-pronged approach that includes predicting misinformation both at the content level and at the entity level:

• Entity prediction: We could use ML at the origination source: for example, predict which users and pages are more likely to share false content, based on the profile of page administrators, the behavior of the page, and its geographical location. We could also expand this to predict which users are most likely to unintentionally spread fake news – not with malicious intent, but still contributing to the problem. I would suggest assigning a trust score to every entity based on features like rate of posts flagged as misinformation and rate of friend request rejections.

• Content prediction: Another approach would be to predict if the content itself would be categorized as fake news, depending on the message in the post, reactions, virality pattern, and other signals. If the post contains a high number of angry reactions or charged responses, or if the virality pattern differs from how other posts spread, this may be an indication of fake news. Content prediction would require considerable use of natural language processing.

For use of algorithms, we could use semi-supervised learning for both cases: since we have few actual labels for the training data, one could manually label fake news (led by a team of subject-matter experts), then use clustering to extrapolate the labels to posts based on similar characteristics (e.g. key words, length, origination source), then allow the supervised learning classifier algo to train based on a much larger dataset.

Can you walk me through the architecture of the neural network that you would use?
Sure, I would ensure the network has the appropriate number of hidden layers, including convolutional layers to allow for feature extraction and pooling and flattening layers to appropriately compress the large input data into a more manageable representation, hopefully with limited loss of information (entropy decline). I would start with a standard activation function like ReLU. At the end of the network, we would use an appropriate activation function to compress the data into plausible values. The output layer would use the softmax activation function (to allow for multi-class prediction rather than the sigmoid activation function used for binary prediction), along with N-1 neurons for every level in the fake news categories we commit to predicting. We could then take argmax of the probabilities for every category to enable hard assignments of each piece of content to a distinct fake news (or not) label.

For the classifier that detects fake news — assuming we’re working with a binary classifier – would you suggest optimizing for precision or recall?
All things considered, I think it’s worse to have undetected fake news in the platform than flagging some authentic content incorrectly with some kind of warning. So optimizing for recall would help us minimize false negatives (fake news going undetected). This would, of course, require a more nuanced conversation with the PM, UX team, and business stakeholders.

Synthesis & Next Steps
These are my main ideas. In summary, I would map out requirements upfront, enlist support from cross-functional stakeholders, prep the data along with feature engineering, and model the solution within a logical filtering framework. There would be various sub-component prediction steps, including using NLP to parse through posts to capture sentiment and topics creating separate prediction flows for fake news propensity for posts versus a fake entity score for users, pages, or groups.

Excellent Answer
The parts of the adequate answer that have been improved are highlighted using italics and bold.

Intro
I can definitely see how fake news detection would be an important use of ML in order to cultivate an environment where users can trust the content they see and engage socially in a way that aligns with the company’s community standards. I’ll walk through how I’d think through the problem and design an end-to-end solution.

Before I jump into some of my ideas, I’d like to ask a few clarifying questions:

• First: In terms of scope, can I assume that fake news could show up across various features of the app, including Feed, Groups, Watch and other parts of the app? [wait for interviewer’s response]

• Second: In terms of timeframe for implementing the solution, I would take a different approach if the goal was to prioritize speed vs building a more robust approach in the long-term. For example, parsing through fake news in text form would be easier than through other content types like images and video. Would you like me to highlight how my approach might change for a short-term vs long-term solution? [wait for interviewer’s response]

• Third: Naturally, having accurate labeling and predictions is just the starting point; how this is handled on the product or on the business side with content moderators, partnerships with academics and beyond would be a whole different angle to the problem. If it works for you, I’ll hone in on just the ML portion but will reference where a hand-off to product or business might be needed. Ok? [wait for interviewer’s response]

Ok, thanks for the additional context. Could I have a moment to gather my thoughts? [wait for interviewer’s response]

Set the Stage
First, we need to be clear about what we are trying to address. Fake news fits into the broader bucket of misinformation, which may be motivated by different use cases but ultimately results in swaying end-users towards a particular nefarious belief, action, or reaction. Needless to say, this is bad for Meta’s overall business as it hampers advertiser and end-user trust. Fake news can take several forms, ranging from instances in which visual or textual information is inserted into an article in order to subtly bias an argument, to wholly fabricated content, often including extraordinary claims created and shared systematically with the sole intention to deceive.

Diving into the specifics:

• Content may originate on the FB platform itself or be reposted from an external source. In terms of reach, the content can spread algorithmically, by a malicious actor, or inadvertently by reposting users who don’t discern between real news vs fake news.

• Within the FB platform, content may show up on different features: Feed, Groups, Pages, Watch tab, Marketplace listings, or even through ads – all with different flavors depending on the product surface. Fake content captures attention and spreads rapidly in comparison to real news.

• In terms of the content itself, it may fall on extremes with egregious content that is blatantly wrong or has malicious intent, or the content may relate to a more subjectively bad topic (context matters). Some of this would directly violate Facebook’s Community Standards Policy and other content would not but may still require removal. Cultural considerations would also impact interpretation and impact of the content. The content format may be text, image, audio, or video-based.

Ok with this context defined, I’d suggest diving into some of these specifically as they apply to the ML solution. For example, later I’ll refer to how the ML solution would be operationalized differently for text vs image-based content.

There are several things we need to accomplish and I plan on tackling this case in a few steps:

• In the problem space, so to speak, we would need to source the foundational data that we would use for ML models, along with feature engineering to steps to enrich the input data.

• In the solution space, so to speak, I’d like to dive into the actual use of algorithms and how the solution would be built based on various sub-models

• Once we have a solution in mind, we can move to the operationalization phase, which is all about scaling the solution in a way that is harmonized with the broader tech stack and having a game plan for measurement, continuous retraining, and ongoing monitoring.

Does this sound good to you? If so, I’ll jump into each of these sections next. [wait for interviewer’s response]

Problem Space
Before jumping into technical details of the ML solution, I’d like to clearly define the problem space, which will include defining the building blocks of the end solution, focusing on data collection to prep for the modeling step. I would suggest collecting data by various dimensions in order to be comprehensive in terms of countering fake news. These would include the following:

- Entity attributes:

• I’ll use ‘entity’ to describe the origination of the content on the Facebook platform. This would include users, of course, but also pages, groups, and any other source capable of originating content.

• Post origination source e.g. user vs page origination type, profile picture (copied from other account?), FB tenure, number of previous posts, number of friends (and rejection rate) etc
  

- Content attributes:

• Macro identifiers: This would include macro identifiers, such as origination date and content type (eg text, photo, video)

• Meaning and intent: We could source data on length of post, inclusion of specific catch phrases (e.g. hot topic words like “vaccine”, “COVID”, “election” etc), inclusion of hyperlinks

• Reactions: number of comments, reactions, user reported content (eg. people flagging a certain post or image and marking it as fake)

- Network spread pattern:

• Beyond the content itself, a key determinant of fake news might be the virality spread pattern via Facebook’s social graph. Perhaps fake news spreads faster and in a more unique pattern (eg less close friends engaging etc). I’d want to track impressions and reactions by minute, number of re-shares, and rate of impressions and engagement classified by distance in the network graph (eg primary connection, 2nd order connection etc). Naturally, I’d have to work closely with ML engineers who own the logic for content distribution, as this would factor into the spread pattern of fake news.

- External datasets:

• I would also consider external datasets (eg Facebook’s "community notes") and fact-check websites we may be able to access to serve as ground truth for what news and sites are considered legitimate.

Once we have the basic data available, there are many ways that we could enrich the data. I would work in an iterative approach across data prep and modeling to ensure that we’re not over-optimizing in the data prep stage. That said, I do think that text mining the content effectively as a feature engineering step could ultimately make or break the model’s quality because we need to be able to capture topics and sentiment of posts. We could start simple with unsophisticated Bag of Words to understand appearance count of each word, N-Grams to capture important combination of words, and TF-IDF to understand the importance of words or N-Grams within the broader context. Longer term, however, I would suggest more robust methods like Long Short Term Memory with proper input/ output/ forget gating throughout the recurrent neural network architecture to preserve salient information while preventing the common issue of vanishing gradients. Of course, we could also consider state of the art approaches like Transformers to allow for speedier training and greater contextual awareness. Ultimately, I would expect the algorithm to yield the sentiment and a list of topics associated to each post that could serve as inputs for our model.

As part of the problem space, I would also work with other stakeholders to share the intention of the project upfront and call out any dependencies for which cross-functional collaboration may be needed. For example, we’d have a heavy dependency on data engineers to set up the foundational data, backend and frontend engineers on any new telemetry requirements needed, MLops on resource requirements for the subsequent modeling step etc.

Solution Space
Ok, now that we’ve understood the problem space and considered the building blocks for the end-solution, I’ll now jump into how I’d leverage ML at scale for fake news detection. I would envision a two-pronged approach that includes predicting misinformation both at the content level and at the entity level:

• Entity prediction: We could use ML at the origination source: for example, predict which users and pages are more likely to share false content, based on the profile of page administrators, the behavior of the page, and its geographical location. We could also expand this to predict which users are most likely to unintentionally spread fake news – not with malicious intent, but still contributing to the problem. I would suggest assigning a trust score to every entity based on features like rate of posts flagged as misinformation and rate of friend request rejections.

• Content prediction (text): Another approach would be to predict if the content itself would be categorized as fake news, depending on the message in the post, reactions, virality pattern, and other signals. If the post contains a high number of angry reactions or charged responses, or if the virality pattern differs from how other posts spread, this may be an indication of fake news. Content prediction would require considerable use of natural language processing. Treating each piece of content independently, though, would be a mistake. We could, instead, detect whether new content is actually merely a new variation of content that independent fact-checkers have already debunked. We would also need to take into account different languages that show up on Facebook; we could use embedding techniques that merge multiple languages into a single shared embedding space, allowing us to more accurately evaluate semantic similarity of sentences.

• Content prediction (image and video): Content including images or video would benefit from computer vision techniques that allow a model to focus on specific key objects within the image while ignoring background clutter. This would help us identify previously reported content that has been slightly modified in order to come across as new. Retraining speed is also imperative. When a new deepfake video is detected, for example, we can leverage these as training data in order to generate new, similar deepfake examples to serve as large-scale training data for our deepfake detection model. This would be an effective use of generative adversarial networks (GANs), a machine learning architecture where two neural networks (generator and discriminator) compete with each other to produce realistic content. This approach would help us continuously fine-tune our system so it is more robust and generalized for dealing with future deepfakes.

For use of algorithms, we could use semi-supervised learning for both cases: since we have few actual labels for the training data, one could manually label fake news (led by a team of subject-matter experts), then use clustering to extrapolate the labels to posts based on similar characteristics (e.g. key words, length, origination source), then allow the supervised learning classifier algo to train based on a much larger dataset.

In the short-term, we would likely prioritize speed over functionality by limiting predictions to the probability of being binary “fake news” or “not fake news” and we would likely limit content to text-based posts, with a greater reliance on manual human moderation to complement. In the long-run, however, I think we could scale the approach to categorize content across more categories like “authentic content”, “violence”, “adult content,” “political misinformation”, “financial fraud” etc in order to allow more targeted tracking and mitigations. We would also want to include non-text based content to be included in the prediction model; this would require extensive use of convolutional neural networks.

Can you walk me through the architecture of the neural network that you would use?
Sure, I would ensure the network has the appropriate number of hidden layers, including convolutional layers to allow for feature extraction and pooling and flattening layers to appropriately compress the large input data into a more manageable representation, hopefully with limited loss of information (entropy decline). We’d also want to experiment with different dropout rates across neuron connections to prevent overfitting. I would start with a standard activation function like ReLU, but would experiment with other options like TanH or leaky ReLU as needed. I would suggest using the efficient Adam gradient descent optimizer along with a categorical crossentropy loss function throughout the forward and backpropagation training process. At the end of the network, we would use an appropriate activation function to compress the data into plausible values. The output layer would use the softmax activation function (to allow for multi-class prediction rather than the sigmoid activation function used for binary prediction), along with N-1 neurons for every level in the fake news categories we commit to predicting. We could then take argmax of the probabilities for every category to enable hard assignments of each piece of content to a distinct fake news (or not) label.

For the classifier that detects fake news — assuming we’re working with a binary classifier – would you suggest optimizing for precision or recall?
All things considered, I think it’s worse to have undetected fake news in the platform than flagging some authentic content incorrectly with some kind of warning. So optimizing for recall would help us minimize false negatives (fake news going undetected). This would, of course, require a more nuanced conversation with the PM, UX team, and business stakeholders. What we optimize for largely depends on the subsequent action that is taken based on the prediction. For example, if we automatically take down content that we believe is fake news, this would be a much more aggressive approach than just keeping the content up and labeling the post “potential fake news; be cautious”. We’d have to be a lot more confident about the post being fake news in the former scenario. If we take a more aggressive approach like taking down posts automatically, we should optimize for precision to reduce false positives where authentic content is miscategorized; if we just flag content in the UI as being questionable, we can optimize for recall in order to have greater coverage, avoiding false negatives where fake news goes undetected.

Synthesis & Next Steps
These are my main ideas. In summary, I would map out requirements upfront, enlist support from cross-functional stakeholders, prep the data along with feature engineering, and model the solution within a logical filtering framework. There would be various sub-component prediction steps, including using NLP to parse through posts to capture sentiment and topics creating separate prediction flows for fake news propensity for posts versus a fake entity score for users, pages, or groups. Naturally, how the predictions are used (eg taking content down or transferring to a content moderation team) would be an important step to clarify with the PM. I would also consider ways to scale the solution: perhaps we could use different sample rates for content to be processed depending on the predicted integrity of the entity, along with standard parallel computing approaches (eg content vs entity prediction can happen in parallel), and batched processing at a cadence that balances speed vs resource requirements.

In terms of next steps, I would map out the plan for scaling, productization, retraining, and continuous monitoring of the solution. I would suggest implementing a simple binary prediction solution capable of working with text-based content first, and then expand the ability to work with multimedia sources like photos and video across more relevant prediction categories. I would work closely with engineers to integrate the solution within the broader tech stack: the UI would need to fetch the fake news predictions for each piece of content with low latency. We could make these predictions available via a REST API (eg built via Flask) via a dedicated ‘content prediction service’ for a simple hand-off to other services that may need to consume the predictions to take down in-product content or relay to the content moderation team.

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