FAANG-Standard Interview Preparation Guide: Applied Scientist (Entry Level)

Prepare 2-3 thoughtful questions about the research focus areas, team structure, or typical projects Applied Scientists work on at the company.

Demonstrate clear, concise communication. Avoid technical jargon unless necessary. Maintain professional tone and show respect for the recruiter's time.

Show that you understand the distinction between pure research and applied research, and can articulate what an Applied Scientist does: designing algorithms for real problems, prototyping, collaborating with engineers, and shipping solutions.

Articulate your ML/AI educational background, relevant coursework, academic projects, internships, and any publications or open-source contributions clearly and concisely.

Clearly express why you're interested in applied research, what excites you about solving real-world ML problems, and why this specific company appeals to you.

Be comfortable writing simple Python code: working with lists, dictionaries, basic NumPy operations (creating arrays, indexing, basic linear algebra), and simple Pandas DataFrames (loading data, filtering, grouping).

Given a problem statement and dataset description, demonstrate the ability to identify the right ML approach, propose an algorithm, explain evaluation strategy, and identify potential challenges.

Understand evaluation metrics (accuracy, precision, recall, F1-score, ROC-AUC, MSE, MAE) and when to use each. Understand cross-validation, train-test splitting, and the importance of not overfitting to test data.

Understand the bias-variance tradeoff conceptually and mathematically. Know how regularization (L1, L2) helps control model complexity and prevent overfitting.

Understand the mechanics, use cases, and limitations of key algorithms: linear regression, logistic regression, decision trees, random forests, k-nearest neighbors (KNN), k-means clustering, and basic neural network concepts.

Understand the core distinction between supervised learning (predicting labels with labeled data) and unsupervised learning (finding patterns in unlabeled data). Know examples of each type.

Practice explaining your experimental results, insights, and limitations clearly and convincingly. Discuss how you'd present findings to different audiences (engineers, business stakeholders, researchers).

When proposing solutions, identify limitations of your approach, computational or resource constraints, and potential trade-offs. Discuss alternative solutions and when each might be preferable.

Show the ability to propose novel approaches to research problems: designing new algorithms, combining existing techniques in new ways, or identifying where current approaches fall short.

Understand statistical significance, p-values, confidence intervals, and how to design and evaluate A/B tests. Know when you have enough data to make conclusions.

Given an ambiguous research problem, demonstrate the ability to break it down into smaller, well-defined sub-problems; identify assumptions; and propose a structured investigation plan.

Understand how to structure experiments: formulating hypotheses, controlling variables, designing treatments and controls, determining sample sizes, and avoiding common pitfalls like confounding variables and p-hacking.

Write efficient, readable, and well-structured Python code. Understand data structures, time/space complexity, debugging techniques, and testing practices. Handle errors gracefully.

Understand how to select hyperparameters using grid search, random search, or other methods. Know how to balance model complexity, training time, and performance. Understand when to use different hyperparameters.

Given a real-world problem description, identify the ML formulation, propose appropriate algorithms, discuss data requirements, and anticipate practical challenges like class imbalance, scalability, or model drift.

Understand how to handle missing values, scale features, encode categorical variables, create new features, and detect and handle outliers. Know when and why each technique is appropriate.

Understand the full pipeline: data loading and exploration, feature engineering, model training, hyperparameter tuning, model evaluation, and result interpretation. Practice building complete pipelines using Scikit-learn and Pandas.

Be able to implement common algorithms without libraries: decision tree training with information gain, k-nearest neighbors classification, linear regression with gradient descent, or basic neural network forward/backward pass.

Understand when and how to use pre-trained models, how to fine-tune them for new tasks, and the benefits of transfer learning for reducing training time and data requirements.

Understand RNNs, LSTMs, and GRUs. Know the vanishing gradient problem and how LSTMs address it. Understand applications to time series and sequential data.

Understand batch normalization, dropout, weight decay, early stopping, and data augmentation. Know how these techniques prevent overfitting and improve training stability.

Understand the self-attention mechanism, multi-head attention, positional encoding, and the Transformer architecture. Know why Transformers have become dominant in NLP and vision.

Understand convolution operations, pooling, stride and padding concepts. Know why CNNs are effective for image data and common architectures (VGG, ResNet, Inception). Be able to explain how they extract features.

Understand forward propagation, backpropagation, activation functions (ReLU, sigmoid, tanh), loss functions (cross-entropy, MSE), and optimization methods (SGD, Adam, momentum).

Understand the journey from research prototype to production system: computational requirements, scalability, integration with existing systems, monitoring, and maintenance considerations.

Prepare a 10-15 minute presentation of one of your most challenging ML/AI projects: problem statement, approach, experiments, results, and what you learned. Be ready for detailed questions.

When multiple approaches exist, analyze trade-offs: accuracy vs. speed, model complexity vs. interpretability, development time vs. optimal results. Make justified decisions considering constraints.

Understand how to frame an applied research problem: defining objectives, identifying constraints (computational, data, timeline), and setting realistic success criteria. Know how to identify what makes a problem worth solving.

Be able to propose novel approaches to problems: combining existing techniques in new ways, identifying where current approaches fall short and how to improve them, and designing systems that leverage recent advances.

Design experiments to validate novel approaches: selecting metrics, setting up baselines, controlling for confounds, and determining when you have sufficient evidence. Discuss potential pitfalls and how to avoid them.

Articulate your specific interest in applied research: why solving real-world problems using ML/AI excites you, and why this company's work in particular aligns with your interests.

Share examples of times you went beyond assigned work: self-directed learning, exploring new ideas, taking on additional challenges, or proposing improvements.

Share examples of effective collaboration: working with teammates, mentors, or colleagues from different backgrounds/disciplines. Discuss how you communicate technical ideas to non-experts.

Discuss a project that didn't go as planned, a research direction that didn't pan out, or critical feedback you received. Explain how you handled it and what you learned.

Discuss how you approach learning new technologies, frameworks, or mathematical concepts. Share examples of times you learned something outside your comfort zone and how you did it.

Share a specific example of a technical challenge you faced, how you approached solving it (including any false starts), what you learned, and how that experience changed your approach to similar problems.