Data Scientist

We are looking for a Senior Data Scientist to build, scale, and optimize the core machine learning and data solutions powering Maya's marketing and personalization platforms. In this role, you will operate as a high-impact technical builder who bridges complex business requirements directly into robust, production-grade ML solutions.

Your primary focus will be engineering and technical excellence: designing sophisticated models mainly around but not limited to, propensity, recommendation, optimization and clustering models, building scalable and cost-optimized data pipelines, writing production-grade code, and taking deep technical ownership of model validation, code quality, and automated deployment cycles.

Nature of Work & Key Responsibilities

• Translating Business Logic to Advanced ML: Analyze marketing and growth objectives to independently architect, train, validate, and deploy high-performing machine learning solutions (including supervised, semi-supervised and supervised models).
• End-to-End Technical Ownership & Pipeline Architecture: Build scalable, cost-optimized data pipelines processing structured and unstructured data. Handle data modeling, schema design, pipeline materialization, scheduling, wiring new data sources, and backfilling.
• Rigorous Model Validation & Business Physics Alignment: Execute deep technical validation to ensure model logic adheres to real-world operational constraints, handling edge cases, preventing data drift, and maintaining internal governance checks.
• Software Engineering & Code Quality: Write clean, modular, and performant code adhering to software engineering best practices. Does peer code reviews, refactor legacy code, and manage technical debt by systematically removing unused pipelines.

Required Qualifications

• Education: Bachelor's or advanced degree in a quantitative discipline (Computer Science, Statistics, Mathematics, Data Science, Physics, Industrial Engineering, or a related field).
• Experience: Minimum 4–6 years of hands-on data science and engineering experience, with strong exposure to marketing analytics, CRM data, or customer lifecycle systems.
• Full-Stack Technical Depth: Demonstrated capability to write production Python/SQL code, build and debug data pipelines, execute rigorous data quality/validation checks, and independently deploy models into production environments.