ML Engineer Data Scientist

The ML Engineer will build and deploy models across classification, regression, clustering, time-series forecasting, and anomaly detection using Python and ML libraries. Responsibilities include managing the full ML lifecycle from data collection to deployment and monitoring, applying MLOps practices including experiment tracking and CI/CD, working with large datasets using SQL, and translating model outputs into business impact. The role requires strong grounding in statistics, probability, and experiment design. The successful candidate will engage closely with business stakeholders to translate analytical requirements into robust, production-ready data solutions that support long-term organizational growth. This position also involves supporting knowledge-sharing sessions, contributing to internal best-practice documentation, and helping to build a culture of continuous learning within the data science team.

3-8 years of experience in Data Science or Machine Learning with strong proficiency in Python, SQL, and ML libraries. Experience with deep learning architectures, MLOps, and production deployment. B.Tech/B.E. qualification. The ideal candidate will bring a proactive, solutions-oriented approach, strong organizational skills, and a commitment to delivering high-quality outcomes within agreed timelines. A strong customer-first attitude and willingness to travel to client locations as needed will be an added advantage for this role. Familiarity with change management practices and a track record of successful stakeholder engagement across diverse business units is preferred.