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 robust 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 (Good to Have) Exposure to building LLM-based applications and RAG pipelines using vector databases (FAISS, AI Search, OpenSearch, 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 / SageMaker / 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. Whats in it for me Work on high-visibility projects that shape organizational strategy Opportunities to mentor peers and grow your leadership capabilities Access to advanced tools, platforms, and professional development resources A culture that celebrates innovation, collaboration,



and technical excellence Bring your expertise to a team thats ready to make an impact together-apply today! 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 (Good to Have) Exposure to building LLM-based applications and RAG pipelines using vector databases (FAISS, AI Search, OpenSearch, 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 / SageMaker / 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.



Whats in it for me Work on high-visibility projects that shape organizational strategy Opportunities to mentor peers and grow your leadership capabilities Access to advanced tools, platforms, and professional development resources A culture that celebrates innovation, collaboration, and technical excellence Bring your expertise to a team thats ready to make an impact together-apply today! Rewards designed for you Our Total Rewards is our way of saying: We see you and we value you. It s more than just pay and benefits-it s a meaningful investment in you. It is designed to help you perform, grow, feel protected, and elevate your potential. The package helps you care for yourself and your family, plan your future, grow your skills and career, collaborate in an inclusive workplace, and contribute to your community. At Schneider Electric, we re here for what matters most to you. Discover more at our Career Page. Country-specific programs and initiatives may be available. Looking to make an IMPACT with your career When you are thinking about joining a new team, culture matters. At Schneider Electric, our values and behaviors are the foundation for creating a great culture to support business success. We believe that our IMPACT values - Inclusion, Mastery, Purpose, Action, Curiosity, Teamwork - starts with us. IMPACT is also your invitation to join Schneider Electric where you can contribute to turning sustainability ambition into actions, no matter what role you play. It is a call to connect your career with the ambition of achieving a more resilient, efficient, and sustainable world. We are looking for IMPACT Makers; exceptional people who turn sustainability ambitions into actions at the intersection of automation, electrification, and digitization. We celebrate IMPACT Makers and believe everyone has the potential to be one. Become an IMPACT Maker with Schneider Electric - apply today! 40 billion global revenue +9% organic growth 150 000+ employees in 100+ countries You must submit an online application to be considered for any position with us. This position will be posted until filled. Schneider Electric aspires to be the most inclusive and caring company in the world, by providing equitable opportunities to everyone, everywhere, and ensuring all employees feel uniquely valued and safe to contribute their best. We mirror the diversity of the communities in which we operate, and inclusion is one of our core values. We believe our differences make us stronger as a company and as individuals and we are committed to championing inclusivity in everything we do. At Schneider Electric, we uphold the highest standards of ethics and compliance, and we believe that trust is a foundational value. Our Trust Charter is our Code of Conduct and demonstrates our commitment to ethics, safety, sustainability, quality and cybersecurity, underpinning every aspect of our business and our willingness to behave and respond respectfully and in good faith to all our stakeholders. You can find out more about our Trust Charter here Schneider Electric is an Equal Opportunity Employer. It is our policy to provide equal employment and advancement opportunities in the areas of recruiting, hiring, training, transferring, and promoting all qualified individuals regardless of race, religion, color, gender, disability, national origin, ancestry, age, military status, sexual orientation, marital status, or any other legally protected characteristic or conduct.

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