: Data Analysis & Exploration: Collect, clean, and explore structured and unstructured datasets to uncover patterns and insights. Model Development: Build, evaluate, and deploy predictive models and statistical algorithms. Experimentation: Design and analyze A/B tests and controlled experiments to assess product and feature performance. Business Insights: Translate analytical findings into actionable recommendations that influence business and product decisions. Collaboration: Partner with data engineers, product managers, and software engineers to integrate ML models and analytical pipelines into production systems. Continuous Learning: Stay up to date on new methods in machine learning, statistics, and data science tools, and apply them to improve workflows. Data Quality & Governance: Establish best practices for data validation, schema management, and observability to ensure data consistency and reproducibility. Performance Optimization: Profile data processes and improve throughput...