Machine Learning & AI Topics
Production machine learning systems, model development, deployment, and operationalization. Covers ML architecture, model training and serving infrastructure, ML platform design, responsible AI practices, and integration of ML capabilities into products. Excludes research-focused ML innovations and academic contributions (see Research & Academic Leadership for publication and research contributions). Emphasizes applied ML engineering at scale and operational considerations for ML systems in production.
Online Experimentation and Model Validation
Running experiments in production to validate model changes and measure business impact. Topics include splitting traffic across model variants canary deployments and champion challenger testing selecting metrics that capture both model performance and business outcomes performing sample size and test duration calculations accounting for statistical power and multiple testing adjustments and handling instrumentation and novelty bias. Candidates should be able to analyze heterogeneous treatment effects monitor experiments in real time and design ramping plans and rollback guardrails to protect user experience and business metrics. The topic also covers decision rules for when to rely on offline evaluation versus online experiments and how to interpret differences between offline model metrics and live user outcomes as part of model validation and deployment strategy.
Machine Learning Fundamentals
Core machine learning concepts and terminology for conceptual understanding. Topics include supervised and unsupervised learning, regression and classification problems, training validation and test splits, cross validation, loss functions and optimization at a high level, model evaluation metrics, overfitting and underfitting, regularization concepts, and common basic model families such as linear models decision trees nearest neighbors and simple neural networks. Emphasis is on conceptual explanations and trade offs rather than deep mathematical derivations.
Model Evaluation and Validation
Comprehensive coverage of how to measure, validate, debug, and monitor machine learning model performance across problem types and throughout the development lifecycle. Candidates should be able to select and justify appropriate evaluation metrics for classification, regression, object detection, and natural language tasks, including accuracy, precision, recall, F one score, receiver operating characteristic area under the curve, mean squared error, mean absolute error, root mean squared error, R squared, intersection over union, and mean average precision, and to describe language task metrics such as token overlap and perplexity. They should be able to interpret confusion matrices and calibration, perform threshold selection and cost sensitive decision analysis, and explain the business implications of false positives and false negatives. Validation and testing strategies include train test split, holdout test sets, k fold cross validation, stratified sampling, and temporal splits for time series, as well as baseline comparisons, champion challenger evaluation, offline versus online evaluation, and online randomized experiments. Candidates should demonstrate techniques to detect and mitigate overfitting and underfitting including learning curves, validation curves, regularization, early stopping, data augmentation, and class imbalance handling, and should be able to debug failing models by investigating data quality, label noise, feature engineering, model training dynamics, and evaluation leakage. The topic also covers model interpretability and limitations, robustness and adversarial considerations, fairness and bias assessment, continuous validation and monitoring in production for concept drift and data drift, practical testing approaches including unit tests for preprocessing and integration tests for pipelines, monitoring and alerting, and producing clear metric reporting tied to business objectives.
Predictive Analytics and Risk Identification
How you use data patterns to predict churn risk, identify expansion opportunities, and proactively engage customers before issues escalate. Discussion of machine learning or predictive models you've implemented.
Feature Engineering and Selection
Covers the end to end process of transforming raw data into predictive and stable model inputs and choosing the most useful subset of those inputs. Topics include generating features from domain signals and timestamps, numerical transformations such as scaling binning and logarithmic transforms, categorical encodings including one hot and target encoding, creation of interaction and polynomial features, construction of dense feature vectors for model consumption, handling missing values and outliers, and strategies for class imbalance. Also includes feature selection and dimensionality reduction methods such as filter techniques statistical tests wrapper methods embedded model based selection mutual information analysis and tree based importance measures. Emphasis is placed on avoiding data leakage validating feature stability over time interpreting feature contributions and documenting rationale for feature creation or removal. For senior roles include designing feature engineering best practices mentoring others and considering feature impact on model interpretability and business metrics.
End to End Machine Learning Problem Solving
Assesses the ability to run a complete machine learning workflow from problem definition through deployment and iteration. Key areas include understanding the business or research question, exploratory data analysis, data cleaning and preprocessing, feature engineering, model selection and training, evaluation and validation techniques, cross validation and experiment design, avoiding pitfalls such as data leakage and bias, tuning and iteration, production deployment considerations, monitoring and model maintenance, and knowing when to revisit earlier steps. Interviewers look for systematic thinking about metrics, reproducibility, collaboration with data engineering teams, and practical trade offs between model complexity and operational constraints.
Bias Identification and Mitigation
Recognizing and mitigating bias in experiments, data, models, and decision processes. Candidates should be able to identify common sources of bias such as selection bias, sampling bias, temporal effects, confounding variables, and feedback loops, and propose technical and experimental mitigations such as randomization, stratification, control groups, feature auditing, fairness metrics, and monitoring for drift. The topic also covers governance and process controls to reduce bias in measurement and product decisions.
Model Evaluation and Quality Assessment
Covers evaluation methods, metrics, and quality assessment approaches for machine learning models including both predictive models and generative models. Topics include selecting appropriate metrics such as accuracy, precision, recall, F one score, area under curve for ranking, root mean square error and mean absolute percentage error for regression, and the rationale for using multiple metrics and baselines. For generative and large language models, covers automatic metrics such as BLEU, ROUGE, METEOR, semantic similarity scores, LLM based evaluation techniques, human evaluation frameworks, factuality and hallucination checking, adversarial and stress testing, error analysis, and designing scalable, cost effective evaluation pipelines and quality assurance processes.
Decision Trees and Ensemble Methods
How decision trees recursively split data. Hyperparameters: max depth, min samples split, criterion. Ensemble methods: random forests, gradient boosting. Understanding why ensembles work (combining weak learners). Trade-offs: complexity, interpretability, bias-variance. When to use trees vs. linear models.