Lead Data Scientist

Kimberly-Clark Corporation

14 - 23 years

50 - 55 Lacs

bengaluru

Posted:1 hour ago| Platform:

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Skills Required

snowflake continuous integration cd python scikit-learn microsoft azure ci/cd machine learning azure devops data bricks pandas deep learning stakeholder engagement integration pytorch statistical modeling communication skills

Work Mode

Work from Office

Job Type

Full Time

Job Description

Lead the end-to-end development of machine learning and AI solutions, from problem framing to deployment and monitoring.
Architect and implement scalable pipelines using Databricks, Azure ML, and Azure Functions.
Drive innovation in Generative AI and AI agent applications using frameworks like LangChain, Semantic Kernel, and Hugging Face Transformers.
Mentor and guide a team of data scientists, fostering a culture of experimentation, learning, and delivery excellence.
Partner with business leaders across RGM, Sales, IME, and Supply Chain to identify opportunities and translate them into data science solutions.
Ensure model governance, reproducibility, and compliance with responsible AI principles.
Contribute to the evolution of enterprise data science strategy and best practices.

Key Qualifications and Experiences:

Master s or PhD in Data Science, Computer Science, Statistics, or related field.
7+ years of experience in data science roles, including 2+ years in a leadership or mentoring capacity.
Deep expertise in Python and its data science ecosystem (pandas, scikit-learn, PyTorch, etc.).
Proven experience with Databricks, Azure ML, and Azure Functions.
Strong background in machine learning, deep learning, and statistical modeling.
Experience building and deploying Generative AI models and AI agents.
Familiarity with Snowflake, SAP data structures, and enterprise data integration.
Excellent communication and stakeholder engagement skills.
Experience in the CPG, healthcare, or B2B/B2C domains.
Knowledge of MLOps, CI/CD for ML, and model monitoring frameworks.
Exposure to vector databases, RAG pipelines, and multi-agent orchestration tools (e.g., CrewAI, AutoGen).
Understanding of data privacy, ethics, and responsible AI frameworks.

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