Data Scientist

Roles and Responsibilities :

• 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.

Your Profile -

• Minimum 5–7+ years of professional experience in data science, applied machine learning, 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.

Skills -

• 5–7+ years of hands-on experience in data science, ML, applied statistics, or advanced analytics in enterprise or product settings.
• Strong Python skills and experience with data preparation, model training, evaluation, and interpretation workflows.
• 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 MLflow, 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.