Overview An accomplished Expert Data Scientist with 10+ years of experience, specializing in classical machine learning, statistical modeling, and end-to-end ML lifecycle management.

Demonstrated ability to take complete ownership of AI/ML initiatives, mentor teams, and collaborate closely with architects and product managers to drive accountable, high-impact solutions.

Exposure to GenAI and agentic AI systems is an added advantage.

Key Responsibilities Machine Learning & Statistical Modelling (Core Focus) Design, build, and optimize robust ML models across regression, classification, clustering, and time series forecasting problems.

Lead advanced feature engineering, data quality assessments, and exploratory data analysis.

Apply strong statistical methods, experimental design (DOE), and rigorous performance evaluation techniques.

Develop scalable, production-grade ML pipelines with a focus on reliability, maintainability, and performance.

Ownership, Mentorship & Cross-Functional Leadership Take end-to-end ownership of ML solutions—from problem definition to deployment and monitoring.

Mentor junior and mid-level data scientists, fostering best practices in modeling, coding, and experimentation.

Work closely with architects and product managers to define solution design, align with business goals, and ensure delivery accountability.

Drive technical direction and contribute to strategic AI/ML roadmap decisions.

As main DS point-of-contact for a set of use-cases,



contribute to the quarterly planning with load estimation of the Data Science activities Escalate risk to the AI solution leadership when needed Cloud ML-Ops & Quality Implement robust ML-Ops practices including model versioning, monitoring, and handling data/concept drift Ensure high standards of quality through documentation, code reviews, and version control (Git-based workflows).

Work across cloud ecosystems such as AWS, Azure, or Databricks with flexibility to adapt.

GenAI & Agentic AI (Positive to Have) Exposure to building LLM-based applications and RAG pipelines using vector databases (FAISS, AI Search, Open

Search, PGVector, etc.).

Familiarity with agentic system patterns such as tool usage, multi-agent workflows, and planner–executor architectures.

Understanding of integration patterns with enterprise systems via APIs and MCP-based tooling.

Innovation Stay current on advances in classical ML methods and tools, and the broader AI landscape, and apply them to high-value enterprise use cases.

Contribute to anticipation/upstream projects as well with technological & scientific watch, IP valorization (patents, publication) Promote a culture of experimentation, innovation, and responsible AI, with a focus on fairness, ethics, and trust.

Required Skills & Experience 10+ years of experience with strong emphasis on classical ML, statistical modeling, and ML lifecycle ownership.

Proven experience in: Python, PySpark, SQL, Scikit-Learn, XGBoost, LightGBM, Random Forest MLflow / Sage

Maker / Databricks Docker, Git, and modern CI/CD practices Strong foundation in EDA, DOE, and model evaluation techniques to validate hypotheses and improve model performance.

Demonstrated ability to own solutions, mentor teams, and collaborate effectively with cross-functional stakeholders.

Experience in GenAI and agentic AI systems is considered a plus.

📌 Data Analytics - Senior Professional (Bengaluru)
🏢 Schneider Electric
📍 Bengaluru