Applied Scientist (Junior Level) Interview Preparation Guide - FAANG Standards

Applied Scientist

Junior

7 rounds

Updated 6/15/2026

Applied Scientist interviews at FAANG companies follow a rigorous multi-stage process designed to evaluate your ability to conduct applied research, develop ML/AI algorithms, prototype solutions, and collaborate across teams. The process typically spans 4-6 weeks and includes initial recruiter screening, technical phone rounds assessing ML fundamentals and coding proficiency, and comprehensive onsite rounds covering research problem-solving, system design for ML systems, statistical analysis, and behavioral/cultural fit. For junior-level candidates, the focus is on demonstrating solid fundamentals, hands-on experience with real projects, ability to work independently with occasional guidance, and strong learning potential.

Interview Rounds

Recruiter Screening

30 min3 focus topicsbehavioral

Technical Phone Screen - Machine Learning Fundamentals and Statistics

45 min5 focus topicstechnical

What to Expect

This technical phone interview assesses your foundational knowledge of machine learning concepts and statistical reasoning. You'll be asked to explain core ML algorithms, their applications, probability theory, hypothesis testing, and statistical inference. The interviewer will evaluate your understanding of when and why you'd choose specific approaches, your ability to explain concepts clearly, and your grasp of bias-variance tradeoff and model evaluation. Expect questions that start with fundamentals but may probe deeper into your understanding.

Tips & Advice

Focus on understanding the 'why' behind algorithms, not just the 'what'. Be prepared to derive or explain key equations for common models. Practice explaining ML concepts at different levels of complexity - you should be able to explain logistic regression to both a technical audience and a product manager. Use whiteboard or shared document to sketch diagrams when explaining concepts. When asked about your projects, focus on the ML methodology: problem definition, feature engineering, model selection, evaluation metrics, and results. For junior level, it's acceptable to say 'I wasn't sure about X so I researched it' - this shows good learning habits. Always validate assumptions and clarify ambiguous questions before diving into answers.

Focus Topics

Unsupervised Learning and Dimensionality Reduction

Clustering methods (K-means, hierarchical), PCA, t-SNE, and when to use each for exploratory analysis and feature engineering

Practice Interview

Study Questions

Bias-Variance Tradeoff and Model Evaluation

Understanding overfitting vs underfitting, regularization techniques, cross-validation, and appropriate evaluation metrics for different problem types

Practice Interview

Study Questions

Supervised Learning Algorithms

Linear regression, logistic regression, decision trees, ensemble methods (random forests, gradient boosting), and when to apply each

Practice Interview

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Hypothesis Testing and Statistical Inference

p-values, confidence intervals, significance levels, Type I/II errors, power analysis, and interpreting experimental results

Practice Interview

Study Questions

Probability and Bayesian Reasoning

Conditional probability, Bayes' theorem, prior and posterior probability, and application in ML contexts like classification

Practice Interview

Study Questions

Technical Phone Screen - Coding and Data Structures

60 min5 focus topicstechnical

What to Expect

This round evaluates your programming proficiency in the context of data science and machine learning. You'll solve coding problems that may involve data manipulation, algorithm implementation, or optimization. Problems are typically medium difficulty and focus on your ability to write clean, efficient code; think through edge cases; and communicate your approach. You may be asked to implement ML-adjacent algorithms, work with data structures efficiently, or optimize solutions. The interviewer is assessing problem-solving approach as much as the final solution.

Tips & Advice

Write pseudocode or outline your approach before jumping into implementation. Explain your thought process as you code - this helps the interviewer follow your logic and provide hints if needed. Start with a clear, straightforward solution even if it's not optimal - then discuss optimization. For junior level, an O(n²) solution that you can explain is better than stumbling through an O(n) approach you don't fully understand. Test your code with examples including edge cases. If you get stuck, ask clarifying questions and discuss your approach with the interviewer rather than sitting in silence. Practice on platforms like LeetCode (medium difficulty) and focus on problems involving arrays, hashmaps, sorting, and basic dynamic programming. Be comfortable coding in Python, as it's the standard for ML roles.

Focus Topics

Basic Dynamic Programming

Recognizing DP problems, memoization vs tabulation, solving problems like climbing stairs, coin change, and longest subsequence

Practice Interview

Study Questions

Sorting and Searching

Common sorting algorithms (merge sort, quicksort conceptually), binary search, and when to apply each

Practice Interview

Study Questions

Hash Maps and Sets

Hash table operations, collision handling conceptually, and using maps/sets for efficient lookups and deduplication

Practice Interview

Study Questions

Arrays and String Manipulation

Working with arrays, lists, string operations, slicing, and common patterns like two-pointer techniques and prefix sums

Practice Interview

Study Questions

Coding Best Practices

Writing readable code, handling edge cases, debugging systematically, and explaining your reasoning clearly

Practice Interview

Study Questions

Onsite Round 1 - Applied Research Problem and Algorithm Design

60 min6 focus topicstechnical

What to Expect

This comprehensive onsite round focuses on your ability to approach real-world applied research problems similar to what you'd encounter in the role. You'll be presented with a problem statement (often ambiguous) and asked to design a solution from first principles. This may involve proposing novel algorithms, selecting appropriate techniques, defining metrics, or planning an experimental approach. The interviewer will assess your problem-solving methodology, creativity, technical depth, and ability to make reasonable trade-offs with incomplete information. At the junior level, you're expected to ask good clarifying questions and propose reasonable solutions with clear justification.

Tips & Advice

Start by clarifying the problem: what are the constraints, what does success look like, what data is available, what computational resources matter? Don't jump to solutions. Think out loud and involve the interviewer in your reasoning - they may guide you toward productive directions. For junior level, it's perfectly acceptable to say 'I would research X' or 'I'm not sure about Y but here's my thinking'. Propose multiple approaches and discuss trade-offs before settling on one. Focus on end-to-end thinking: problem definition, approach, implementation strategy, evaluation methodology, and potential challenges. Use your real project experience as anchors - relate the problem to things you've actually done. Draw diagrams to explain your approach. Be prepared for the interviewer to poke holes in your solution and adjust gracefully.

Focus Topics

From Research to Production

Thinking about how research ideas translate to production systems, scalability considerations, and collaboration with engineering teams

Practice Interview

Study Questions

Handling Trade-offs and Constraints

Making informed decisions when optimizing for accuracy vs speed vs interpretability, considering computational constraints and business requirements

Practice Interview

Study Questions

Algorithm and Approach Selection

Knowledge of when to apply different ML techniques, data structures, and algorithmic approaches based on problem constraints and characteristics

Practice Interview

Study Questions

Feature Engineering and Data Representation

Designing effective feature representations, transformations, and selection strategies that capture relevant information for the problem

Practice Interview

Study Questions

Evaluation Metrics and Experimental Design

Selecting appropriate metrics for different objectives, designing validation strategies, and planning how to measure success

Practice Interview

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Problem Decomposition and Clarification

Ability to break down ambiguous problems into well-defined components, ask clarifying questions, and establish clear success criteria

Practice Interview

Study Questions

Onsite Round 2 - Machine Learning System Design

60 min5 focus topicssystem design

What to Expect

This round assesses your ability to design machine learning systems at a higher level. You'll be asked to design the architecture of an ML system that solves a real-world problem at scale (e.g., 'Design a recommendation system for millions of users' or 'Design a fraud detection system'). The focus is on how you think about system architecture, data pipelines, model training and serving, monitoring, and deployment considerations. This differs from coding interviews - you're drawing high-level architecture, not writing code. You should discuss trade-offs between different design choices, scalability challenges, and how to iterate on the system. At junior level, you're expected to think about basic system design - data pipeline, training infrastructure, and serving considerations.

Tips & Advice

Start by clarifying requirements and constraints: scale (users, data volume, queries per second), latency requirements, accuracy requirements, infrastructure constraints. Ask about the problem context before diving into design. Use a structured approach: define the problem, propose a high-level architecture, discuss data pipeline, model training approach, serving strategy, and monitoring. Draw diagrams to illustrate components and data flow. For junior level, don't be expected to know every detail - focus on logical, reasonable design choices with clear justification. Discuss trade-offs: should you use a simple model or complex one? Batch or real-time predictions? This shows thinking beyond 'just make it accurate'. Consider feedback loops and how to monitor model performance in production. Use examples from your experience or things you've researched. Be comfortable saying 'I would need to learn more about X' while proposing reasonable approaches.

Focus Topics

Monitoring, Evaluation, and Feedback Loops

Setting up monitoring for model performance in production, defining success metrics, and designing feedback loops for continuous improvement

Practice Interview

Study Questions

Scalability and Performance Optimization

Designing systems that scale to millions of users/requests, handling large datasets, and optimizing for latency and throughput

Practice Interview

Study Questions

Model Training and Serving Trade-offs

Batch vs online training, batch vs real-time serving, model serving frameworks, and latency/accuracy trade-offs

Practice Interview

Study Questions

ML System Architecture and Design

Designing end-to-end ML systems covering data pipeline, feature engineering, model training, serving, and monitoring components

Practice Interview

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Data Pipeline and Feature Store Considerations

Designing data collection, storage, preprocessing, and feature engineering pipelines that support model development and serving

Practice Interview

Study Questions

Onsite Round 3 - SQL and Data Analysis

60 min5 focus topicstechnical

What to Expect

This technical round evaluates your SQL proficiency and ability to extract insights from data. You'll write SQL queries to answer business questions, perform exploratory data analysis, and demonstrate understanding of data at scale. Problems typically involve joins, aggregations, window functions, and require thinking about query efficiency. This is practical skills assessment - can you extract what you need from production databases? You may also discuss approach to analyzing experimental results or debugging data issues. For junior level, focus on writing correct, readable queries and explaining your analysis approach clearly.

Tips & Advice

Write SQL queries that are readable first - use clear table aliases, meaningful column names, and comments. Start with a simple query and optimize if needed. Explain your approach before writing code: what tables do you need, what joins, what aggregations? Test your logic with examples. For complex problems, break them into steps: first get the subset of data you need, then aggregate, then order. Be comfortable with window functions, subqueries, and CTEs (Common Table Expressions) - these are common in real work. When analyzing results, calculate metrics properly and think about edge cases (null values, zero divisions, etc.). If asked about data quality issues, think systematically: missing data, duplicates, outliers, schema mismatches. Practice on platforms like LeetCode SQL section or StrataScratch. Focus on medium-difficulty problems that require multiple joins and aggregations.

Focus Topics

Exploratory Data Analysis and Insight Generation

Using SQL to understand data distributions, identify anomalies, validate hypotheses, and generate insights for research

Practice Interview

Study Questions

Query Optimization and Performance

Understanding indexing, query execution plans, and writing queries that run efficiently on large datasets

Practice Interview

Study Questions

Joins and Complex Query Logic

Understanding different join types (INNER, LEFT, RIGHT, FULL), subqueries, and combining data from multiple tables correctly

Practice Interview

Study Questions

SQL Fundamentals and Query Writing

Writing correct, efficient SQL queries using SELECT, WHERE, JOIN, GROUP BY, ORDER BY, and HAVING clauses

Practice Interview

Study Questions

Aggregations and Window Functions

GROUP BY operations, window functions (ROW_NUMBER, RANK, LAG, LEAD), and calculating running totals or rankings

Practice Interview

Study Questions

Onsite Round 4 - Behavioral and Leadership

60 min5 focus topicsbehavioral

What to Expect

This final round assesses cultural fit, collaboration style, and how you approach challenges beyond technical execution. The interviewer will ask behavioral questions focused on your past experiences, how you've handled conflicts, learned from failures, collaborated with others, and contributed to team success. At the junior level, the focus is on demonstrating ability to work independently with occasional guidance, positive attitude toward learning, collaboration with team members, and ownership of assigned work. You'll discuss your past projects, challenges you overcame, and how you interact with colleagues. The interviewer is assessing whether you'll fit the team and grow into a strong contributor.

Tips & Advice

Prepare 3-4 concrete project stories from your experience and use them to answer multiple behavioral questions. Use the STAR method: Situation (context), Task (what you were responsible for), Action (what you did), Result (outcome and lessons). Be specific and quantify results when possible. For junior level, it's appropriate to discuss scenarios where you sought help or learned from senior colleagues - this shows good judgment. Talk about technical challenges you solved and collaboration experiences. Discuss how you handle feedback and disagreement - show you're coachable. Prepare for questions about motivations, growth areas, and what success looks like for you. Research the company's values and weave them into your stories naturally. Practice with a friend or mentor. Be authentic - interviewers can tell when you're reciting prepared answers vs. having genuine conversations.

Focus Topics

Communication and Stakeholder Engagement

Explaining technical work to non-technical stakeholders, presenting findings, and gaining buy-in for research directions

Practice Interview

Study Questions

Handling Ambiguity and Setbacks

Navigating unclear requirements, dealing with failed experiments, and adapting when plans change

Practice Interview

Study Questions

Collaboration and Teamwork

Working effectively with team members, communicating progress, integrating feedback, and contributing to team success beyond your individual work

Practice Interview

Study Questions

Project Ownership and Problem-Solving

Demonstrating ability to independently own parts of projects, overcome technical and non-technical challenges, and drive toward completion

Practice Interview

Study Questions

Learning Ability and Growth Mindset

Examples of learning new skills, adapting to new challenges, seeking feedback, and growing from failures or mistakes

Practice Interview

Study Questions

Frequently Asked Applied Scientist Interview Questions

Data Pipelines and Feature PlatformsEasyTechnical

26 practiced

Give an example of a simple streaming topology (sources, transformations, sinks) to compute a feature: 'rolling average purchase amount per user over the last 24 hours'. Specify how you'd handle late arrivals and where state is stored.

Collaboration and Communication SkillsEasyTechnical

64 practiced

Give a concrete example of mentoring a junior applied scientist or intern. How did you structure feedback, set milestones, and measure progress? Include a typical 4-week mentorship plan with meeting cadence, deliverables, code-review practices, and metrics for success.

Feature Engineering and SelectionEasyTechnical

21 practiced

Describe common categorical encoding techniques and when to use them in production: one-hot encoding, ordinal encoding, target (mean) encoding with smoothing and out-of-fold calculation, and learned embeddings. Discuss trade-offs in memory usage, inference latency, interpretability, and the risk of target leakage for each approach.

Model Monitoring and ObservabilityHardSystem Design

61 practiced

Propose an architecture and process to enable 'sample replay' for debugging failed predictions: how to capture, store, and deterministically replay an inference request end-to-end (including preprocessing and feature transforms) in a staging environment.

Learning Agility and Growth MindsetEasyBehavioral

46 practiced

Describe a time when you received critical feedback about a model, experiment, or technical approach and used it to materially improve the outcome. Include: the feedback content, your initial reaction, concrete steps you implemented, how you validated the changes (tests/metrics), and what you changed in your personal process afterward.

Model and Algorithm SelectionMediumTechnical

62 practiced

Design an experimental protocol to compare Random Forest, XGBoost, and a feed-forward neural network on a mid-sized tabular regression dataset. Specify the cross-validation scheme, hyperparameter search strategy and budget, performance metrics to report, statistical testing to compare models, and practical stopping conditions to avoid wasting compute.

Sample Answer

**Experiment overview**Compare Random Forest (RF), XGBoost (XGB), and a feed-forward NN on the same preprocessed mid-sized tabular regression dataset (n ~10k–100k). Goal: robust performance and cost-aware selection.

**CV scheme**- Stratified-ish K-fold: 5× repeated CV (5 folds × 3 repeats) with time-aware folds if temporal. Use same fold splits and preprocessing pipelines (imputation, scaling, categorical encoding) inside each fold.

**Hyperparameter search**- Use nested CV: outer loop = 5-fold for final estimate; inner loop = hyperparameter tuning (Bayesian optimization — e.g., Optuna).- Budget per model: ~100 trials for RF, 200 for XGB, 200 for NN. Early-prune with successive halving / ASHA for NN and XGB to drop poor trials.- Search spaces (example): - RF: n_estimators [100–2000], max_depth [None or 5–50], max_features [sqrt, log2, 0.1–1.0] - XGB: learning_rate [1e-3–0.3], n_estimators [100–2000], max_depth [3–12], subsample, colsample_bytree - NN: layers [1–4], units [32–1024], lr [1e-5–1e-2], batch_size [32–512], weight decay, dropout

**Metrics**- Primary: RMSE and MAE (report both). Secondary: R^2, calibration/residual analysis, training time and inference latency, memory.- Report mean ± 95% CI across outer folds.

**Statistical testing**- Use paired, non-parametric test across outer folds: Wilcoxon signed-rank test on RMSE pairs (model A vs B). Adjust p-values for multiple comparisons (Holm-Bonferroni).- Report effect size (Cohen’s d or rank-biserial).

**Practical stopping conditions**- Stop tuning if validation performance plateaus: no improvement >0.5% in best metric over 50 consecutive trials for XGB/NN.- Budget cap: total compute budget in GPU-hours/CPU-hours; abort NN search if validation loss worse than XGB baseline after 50 trials.- Use early-stopping rounds (XGB) and ASHA to avoid long bad trials.- If model complexity yields negligible gains but large latency/memory cost, prefer simpler model.

**Deliverables**- Table with metrics, CI, p-values, effect sizes, compute costs; learning curves and residual diagnostics; recommended model with trade-offs.

ML Systems Architecture & ComponentsEasyTechnical

74 practiced

Outline a reproducible ML training workflow that aims for deterministic results where possible. Describe how you would capture data snapshots, environment dependencies (e.g., container images), random seeds, deterministic libraries or ops, and approaches to validate reproducibility across machines and runs.

Sample Answer

**Goal & overview**

Provide an end-to-end, reproducible ML training workflow that yields deterministic results where possible by capturing data, code, environment, seeds, deterministic ops, and validation steps.

**1) Data snapshots**- Store raw and processed datasets in immutable object storage (S3/GCS) with versioned paths and content hashes.- Use DVC or Git-LFS to track pointers + metadata (sha256, schema, row counts, sample checksums).- Save exact preprocessing code and random-seeded transforms; snapshot intermediate artifacts.

**2) Environment & dependencies**- Build and publish container images (Docker) with a fixed image tag and digest; store Dockerfile and build context in repo.- Capture language/package manifests (pip freeze / poetry.lock / conda env export) and OS/kernel info.- Record hardware, CUDA/cuDNN versions, GPU model, driver, and BIOS where relevant.

**3) Random seeds & determinism**- Set and record seeds centrally for Python, NumPy, random, and framework RNGs (PyTorch, TensorFlow).- In PyTorch: torch.manual_seed, torch.cuda.manual_seed_all, torch.use_deterministic_algorithms(True), torch.backends.cudnn.benchmark = False.- In TF: tf.random.set_seed and disable nondeterministic ops where possible.- Log seeds in experiment tracking (MLflow/W&B) and the exact training command/args.

**4) Deterministic libraries / ops**- Prefer deterministic implementations or lock to specific library versions where determinism is documented.- Avoid nondeterministic ops (atomic reductions, unordered parallel ops) or replace with deterministic equivalents.- Pin BLAS/MKL/OpenMP settings (OMP_NUM_THREADS) and set environment variables that affect numeric reproducibility (e.g., CUBLAS_WORKSPACE_CONFIG for CUDA).

**5) Experiment tracking & provenance**- Use MLflow/W&B to log config, hyperparameters, dataset hashes, container digest, seeds, and model artifacts.- Store trained model weights with checksums and link to the dataset + image digest.

**6) Validation across machines & runs**- Unit test deterministic components (preprocessing, seed-based sampling) using deterministic inputs and assert checksums.- Re-run training in CI with the same container digest and seeds; compare resulting model checksums, metrics, and hashes.- For cross-machine validation, run small reproducible experiments on two distinct machines and compare logs, random streams, and final artifact hashes; investigate differences in driver/library versions or nondeterministic ops.- Use deterministic smoke tests in PRs and nightly reproducibility runs.

Result: An auditable pipeline where data, code, environment, seeds, and logs are captured; deterministic settings and validation steps minimize run-to-run variance and surface nondeterminism for investigation.

Data Pipelines and Feature PlatformsMediumSystem Design

26 practiced

Design a dataset versioning strategy for a feature platform that supports reproducible experiments and audits. Explain how versions are referenced by models and how storage costs are managed.

Feature Engineering and SelectionMediumTechnical

37 practiced

Compare filter-based, wrapper-based, and embedded feature selection methods for production use. Provide examples (chi-squared, mutual information, univariate tests for filters; RFE and forward selection for wrappers; L1 regularization and tree-based importances for embedded methods), discuss computational complexity and overfitting risk, and recommend when each approach is most appropriate.

Model Monitoring and ObservabilityMediumTechnical

96 practiced

Explain the Population Stability Index (PSI). Provide a step-by-step calculation for PSI between a baseline distribution and a recent window for a binned continuous feature, and discuss limitations of PSI in practice.

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Applied Scientist (Junior Level) Interview Preparation Guide - FAANG Standards

Interview Process Overview

Interview Rounds

Recruiter Screening

What to Expect

Tips & Advice

Focus Topics

Communication and Soft Skills

Practice Interview

Study Questions

Resume and Project Discussion

Practice Interview

Study Questions

Career Motivation and Company Fit

Practice Interview

Study Questions

Technical Phone Screen - Machine Learning Fundamentals and Statistics

What to Expect

Tips & Advice

Focus Topics

Unsupervised Learning and Dimensionality Reduction

Practice Interview

Study Questions

Bias-Variance Tradeoff and Model Evaluation

Practice Interview

Study Questions

Supervised Learning Algorithms

Practice Interview

Study Questions

Hypothesis Testing and Statistical Inference

Practice Interview

Study Questions

Probability and Bayesian Reasoning

Practice Interview

Study Questions

Technical Phone Screen - Coding and Data Structures

What to Expect

Tips & Advice

Focus Topics

Basic Dynamic Programming

Practice Interview

Study Questions

Sorting and Searching

Practice Interview

Study Questions

Hash Maps and Sets

Practice Interview

Study Questions

Arrays and String Manipulation

Practice Interview

Study Questions

Coding Best Practices

Practice Interview

Study Questions

Onsite Round 1 - Applied Research Problem and Algorithm Design

What to Expect

Tips & Advice

Focus Topics

From Research to Production

Practice Interview

Study Questions

Handling Trade-offs and Constraints

Practice Interview

Study Questions

Algorithm and Approach Selection

Practice Interview

Study Questions

Feature Engineering and Data Representation

Practice Interview

Study Questions

Evaluation Metrics and Experimental Design

Practice Interview

Study Questions

Problem Decomposition and Clarification

Practice Interview

Study Questions

Onsite Round 2 - Machine Learning System Design

What to Expect

Tips & Advice

Focus Topics