Data Scientist - Machine Learning/Generative AI

Role summary: You will own the end-to-end delivery of ML- and data-driven capabilities for clientsturning business needs into production-grade data pipelines, deployed models (recommendation / NLP / GenAI), and measurable outcomes. This is a builder + owner role: you will be accountable for technical delivery, operational reliability, and proactive stakeholder expectation management. Total Experience: 8-9 years Core responsibilities: - Own the end-to-end delivery of production-grade ML systems for recommendations, NLP, and personalization. - Take full ownership of scalable data pipelines. Architect, build, and operate robust ETL/ELT systems. - Establish and enforce data architecture standards. Design scalable storage patterns for analytics and ML. - Drive MLOps as a discipline. Build and institutionalize CI/CD-aligned ML workflows. - Deliver production-ready systems. Expose model and data capabilities through versioned APIs. - Own stakeholder outcomes and client success. Must-have Experience: - End-to-end ownership of at least one production ML deliverable. - Hands-on delivery of scalable data pipelines. - Production software practices: testing, CI/CD, API design, monitoring. - Client-facing delivery and stakeholder management. - Practical data-lake experience. Tech Stack & Qualifications: - Bachelors degree in computer science, Data Analytics, Engineering, or a related discipline. - Strong grasp of core data engineering principles, including data lake architectures, columnar storage formats, ETL/ELT frameworks, and BI ecosystems. - Ability to operate effectively in independent and collaborative environments. - Strong written and verbal communication skills. - Sharp critical thinking and structured problem-solving abilities. - High degree of ownership and adaptability.