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.
Multi Armed Bandits and Experimentation
Covers adaptive experimentation methods that trade off exploration and exploitation to optimize sequential decision making, and how they compare to traditional A B testing. Core concepts include the exploration versus exploitation dilemma, regret minimization, reward modeling, and handling delayed or noisy feedback. Familiar algorithms and families to understand are epsilon greedy, Upper Confidence Bound, Thompson sampling, and contextual bandit extensions that incorporate features or user context. Practical considerations include when to choose bandit approaches versus fixed randomized experiments, designing reward signals and metrics, dealing with non stationary environments and concept drift, safety and business constraints on exploration, offline evaluation and simulation, hyperparameter selection and tuning, deployment patterns for online learning, and reporting and interpretability of adaptive experiments. Applications include personalization, recommendation systems, online testing, dynamic pricing, and resource allocation.
Data Preprocessing and Handling for AI
Covers the end to end preparation of raw data for analysis and modeling in machine learning and artificial intelligence. Topics include data collection and ingestion, data quality assessment, detecting and handling missing values with deletion or various imputation strategies, identifying and treating outliers, removing duplicates, and standardizing formats such as dates and categorical labels. Includes data type conversions, categorical variable encoding, feature scaling and normalization, standardization to zero mean and unit variance, and guidance on when each is appropriate given model choice. Covers feature engineering and selection, addressing class imbalance with sampling and weighting methods, and domain specific preprocessing such as data augmentation for computer vision and text preprocessing for natural language processing. Emphasizes correct order of operations, reproducible pipelines, splitting data into training validation and test sets, cross validation practices, and documenting preprocessing decisions and their impact on model performance. Also explains which models are sensitive to feature scale, common pitfalls, and evaluation strategies to ensure preprocessing does not leak information.
AI and Machine Learning Background
A synopsis of applied artificial intelligence and machine learning experience including models, frameworks, and pipelines used, datasets and scale, production deployment experience, evaluation metrics, and measurable business outcomes. Candidates should describe specific projects, roles played, research versus production distinctions, and technical choices and trade offs.
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.
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.
Linear and Logistic Regression Implementation
Covers the fundamentals and implementation details of linear regression for continuous prediction and logistic regression for binary or multiclass classification. Candidates should understand model formulation, hypothesis functions, and the intuition behind fitting a line or hyperplane for regression and using a sigmoid or softmax function for classification. Include loss functions such as mean squared error for regression and cross entropy loss for classification, optimization methods including gradient descent and variants, regularization techniques, feature engineering and scaling, metrics for evaluation such as mean absolute error and accuracy and area under curve, and hyperparameter selection and validation strategies. Expect discussion of practical implementation using numerical libraries and machine learning toolkits, trade offs and limitations of each approach, numerical stability, and common pitfalls such as underfitting and overfitting.
Recommendation and Ranking Systems
Designing recommendation and ranking systems and personalization architectures covers algorithms, end to end system architecture, evaluation, and operational concerns for producing ranked item lists that meet business and user objectives. Core algorithmic approaches include collaborative filtering, content based filtering, hybrid methods, session based and sequence models, representation learning and embedding based retrieval, and learning to rank models such as gradient boosted trees and deep neural networks. At scale, common architectures use a two stage pipeline of candidate retrieval followed by a ranking stage, supported by approximate nearest neighbor indexes for retrieval and low latency model serving for ranking. Key engineering topics include feature engineering and feature freshness, offline batch pipelines and online incremental updates, feature stores, model training and deployment, caching and latency optimizations, throughput and cost trade offs, and monitoring and model governance. Evaluation spans offline metrics such as precision at k, recall at k, normalized discounted cumulative gain, calibration and bias checks, plus online metrics such as engagement, click through rate, conversion and revenue and longer term retention. Important product and research trade offs include accuracy versus diversity and novelty, fairness and bias mitigation, popularity bias and freshness, cold start for new users and items, exploration and exploitation strategies, multi objective optimization and business constraint balancing. Operational considerations for senior level roles include scaling to millions of users and items, experiment design and split testing, addressing feedback loops and data leakage, interpretability and explainability, privacy and data minimization, and aligning recommendation objectives to business goals.