Data Scientist (Mumbai Location)

Thyssenkrupp · Thāne

Design statistical, machine learning, and generative AI solutions for high-value use cases such as forecasting, classification, anomaly detection, recommendations, optimization, and text analytics.
Use Microsoft Fabric to explore data, engineer features, train models, and track experiments with MLflow.
Design experiments and evaluations, interpret results rigorously, and translate findings into trusted recommendations and measurable improvements.
Support generative and agentic AI use cases by evaluating model behavior, improving prompts and grounding, and identifying where models, insights, or agents create value.
Use Microsoft Foundry to prototype, test, optimize, and evaluate AI applications and agents using enterprise data, models, and tools.
Ensure models and analytical products meet governance, privacy, lineage, and responsible AI requirements in collaboration with Purview and governance stakeholders.
Contribute reusable assets such as feature logic, evaluation methods, notebooks, experimentation patterns, and documentation standards.

Minimum 5 to 7+ years of professional experience in data science, ML, applied machine learning, advanced analytics in enterprise or product settings, econometrics, statistics, operations research, or a closely related field.
Proven track record of delivering production-relevant analytical or ML solutions that drive measurable business outcomes.
Experience working with cross-functional teams including data engineering, architecture, product, and business stakeholders.
Experience in regulated, enterprise-scale, or governance-heavy environments is highly desirable.
Solid grounding in statistics, experimentation, predictive modeling, and model evaluation to select fit-for-purpose methods.
Practical Microsoft Fabric experience with notebooks, experiments, analytics assets, and collaboration with data and analytics teams.
Familiarity with ML flow, experiment tracking, and reproducible analytics workflows.
Good understanding of Azure-native AI patterns, including evaluation, observability, grounding, and lifecycle management in Microsoft Foundry.
Ability to embed data science outputs into AI applications, copilots, or agents in collaboration with Copilot Studio or similar teams.
Awareness of governance, information protection, and responsible AI controls for predictive and generative AI use of enterprise data.