What a Lead } should know

Defines algorithmic standards for the data science team, conducts complexity reviews. Establishes guidelines for algorithm selection at different data scales. Coordinates optimization of production ML systems by computational cost.

Defines Code Quality and Refactoring standards at team/product level. Conducts architectural reviews. Establishes best practices and training materials for the team.

Defines data structure standards for the team's ML projects. Establishes guidelines for memory usage optimization in production ML systems. Coordinates data structure selection for scalable feature engineering pipelines.

Defines OOP/SOLID standards for ML research team: experiment pipeline class architecture, model wrapper interface contracts, feature engineering module design guidelines. Conducts reviews balancing OOP rigor with research iteration speed.

Defines architecture for ML model serving and experiment tracking APIs using FastAPI and Django. Establishes standards for prediction endpoints, A/B test result APIs, and feature store integrations. Conducts design reviews and defines the roadmap for ML platform web services.

Defines data strategy at product level. Establishes ClickHouse standards. Conducts data schema and scaling strategy reviews.

Defines data strategy at product level. Establishes Database Indexing standards. Conducts data schema and scaling strategy reviews.

Defines PostgreSQL data strategy for ML teams: establishes standards for feature store design and training data management, designs database architecture for reproducible ML experiments, drives adoption of PostgreSQL best practices for data science workflows.

Defines data strategy at product level. Establishes Query Optimization standards. Conducts data schema and scaling strategy reviews.

Defines API strategy for ML services at product level: model serving API standards, ML pipeline API governance, experiment management endpoint policies. Conducts API architecture reviews for ML platform services and establishes ML API design practices.

Defines AWS infrastructure strategy for data science platforms spanning experiment environments and production ML serving. Establishes IaC standards for SageMaker Studio, distributed training clusters, and experiment reproducibility. Conducts architecture reviews optimizing ML workload costs and coordinates FinOps for data science compute resources.

Defines Docker strategy for ML workflows: GPU container platform governance, experiment environment standardization, model container lifecycle policies. Conducts architecture reviews for containerized ML infrastructure and optimizes FinOps for GPU compute resources.

Defines testing strategy at product level for ML projects: experiment reproducibility testing standards, model validation governance, statistical testing frameworks for model comparison. Establishes shift-left testing culture balancing research speed with production ML code quality.

Defines data engineering strategy. Shapes data platform. Coordinates data teams. Optimizes data mesh/data fabric approaches.

Defines ML dashboard strategy for experiment tracking and model monitoring across data science teams. Shapes the MLOps visualization platform integrating MLflow, W&B, and custom dashboards. Coordinates ML teams on standardized reporting for model performance and feature importance.

Defines data quality strategy for ML teams ensuring high-quality training datasets. Coordinates validation standards across feature stores and model pipelines. Drives adoption of observability tools and quality gates before training. Aligns quality practices with MLOps and data mesh.

Defines data engineering strategy for ML teams. Shapes ML data platform: feature store architecture, data pipeline standards, and training data governance. Coordinates ML teams on shared feature engineering libraries and data quality practices. Drives adoption of modern data processing tools (Polars, Ray) for ML workloads.

Defines ML data platform ETL strategy and feature engineering standards. Governs training data preparation workflows across DS teams, establishes data versioning policies, and coordinates ETL infrastructure for model training at scale.

Defines Classical ML (scikit-learn) strategy at team/product level. Establishes standards and best practices. Conducts reviews.

Defines experiment tracking strategy for the data science function: selects and standardizes tooling (MLflow, W&B, Neptune) across teams, establishes cross-team experiment sharing protocols, and ensures tracking practices support model governance and audit requirements

Defines Feature Stores strategy at team/product level. Establishes standards and best practices. Conducts reviews.

Defines gradient boosting standards for the data science team. Establishes reusable training pipelines for tabular data. Coordinates the choice between gradient boosting and deep learning for different task types and data.

Defines the strategic roadmap for LLM adoption in data science workflows across the organization. Establishes evaluation standards for LLM-augmented analytics including bias detection, hallucination measurement, and domain accuracy benchmarks. Drives build-vs-buy decisions for embedding infrastructure and RAG platforms.

Defines ML pipeline infrastructure strategy for the data science team. Establishes pipeline development, testing, and monitoring standards. Coordinates unification of pipeline approaches across projects and teams.

Defines MLflow tracking standards across data science teams: naming conventions, metric taxonomies, and experiment organization. Drives Model Registry as single source of truth for governance. Reviews experiment design and tracking practices ensuring reproducibility across ML projects.

Defines model monitoring strategy at team and product level. Establishes standards for drift detection thresholds, alerting SLOs, and incident escalation across deployed models. Conducts reviews of monitoring coverage and drives adoption of observability tooling like Evidently or Arize across teams.

Defines model serving strategy for ML teams. Establishes model deployment standards, serving infrastructure requirements, and monitoring governance. Conducts reviews of serving architectures. Drives adoption of MLOps best practices for reliable model deployment across teams.

Defines PyTorch standards for DS team: experiment structure, reproducibility requirements, model versioning. Evaluates ecosystem tools (Lightning, TorchRec) for team adoption. Reviews architectural decisions in training pipelines. Drives knowledge sharing on advanced patterns.

Defines Recommender Systems Fundamentals strategy at team/product level. Establishes standards and best practices. Conducts reviews.

Defines NLP and Transformer strategy at team level: selects model architectures, establishes training infrastructure, and sets quality benchmarks. Conducts architectural reviews of ML pipelines. Drives adoption of best practices for experiment tracking, model versioning, and reproducibility.

Defines code review strategy for ML research team: experiment code review standards, model quality review governance, research-to-production review practices. Establishes review culture balancing research freedom with code quality.