Lead Data Scientist
Ecolab (company site, opens in a new tab)8+ yrsBengaluruOn-siteDirect career pageEst. 30–50 LPA
Posted on 16 Sept
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Job description
Structured overview of role & requirements
About This Role
- Lead end-to-end design, development, evaluation, and deployment of production-grade machine learning models addressing predictive, classification, recommendation, anomaly detection, forecasting, and optimization use cases.
- Develop and operationalize robust, scalable ML pipelines and MLOps practices ensuring reproducibility, maintainability, and measurable business impact.
- Mentor data scientists and ML engineers on modeling rigor, experimentation, production-readiness, and contribute reusable ML assets and best practices across the team.
Minimum Requirements
- 8+ years experience in data science, machine learning, applied AI, or advanced analytics with proven production ML deployment.
- Strong hands-on expertise in Python and ML frameworks (scikit-learn, XGBoost, PyTorch, TensorFlow, etc.) and end-to-end pipeline development including feature engineering and monitoring.
- Practical experience with MLOps tools and platforms such as Azure Machine Learning, Databricks, MLflow, Azure DevOps, Docker, Kubernetes or equivalents.
- Experience with integrating ML models into applications/APIs, working knowledge of data engineering patterns including SQL and streaming, plus understanding of model lifecycle management.
Ideal Candidate Profile
- Experienced technical leader capable of guiding complex ML projects involving cross-functional teams and multiple models from design through operationalization.
- Proficient in advanced MLOps practices and tooling, including experiment tracking, CI/CD for ML, model governance, monitoring, and continuous improvement in a production product environment.
- Strong ability to translate business requirements into scalable ML solutions and lead strategic decisions on model architecture, operationalization, and team mentorship.