Data Science Lead

The role is responsible for leading the end-to-end data science lifecycle from strategic problem identification to technical execution and ensuring that advanced machine learning and optimization solutions drive measurable business value. Key Responsibilities - Align data science initiatives with business goals and identify high-impact use cases. - Actively design, build, and review models (ML, forecasting, optimization), perform complex analyses, and validate results. - Lead development of machine learning, forecasting, and optimization solutions. - Ensure data governance, integrity, and compliance standards. - Implement MLOps practices for deployment, monitoring, and scalability. - Translate business needs into analytical solutions and communicate insights clearly. - Prioritize projects, track KPIs, and ensure measurable business impact. - Promote experimentation, new technologies, and continuous improvement. Requirements: - Bachelor’s degree in Statistics, Data Science, Computer Engineering or Science, Mathematics, Information Technology, Artificial Intelligence. - Minimum +5 years’ experience in data science. - Programming & Data, Python/R, SQL and Azure ML - Machine Learning: Supervised/unsupervised learning, forecasting, optimization - Statistics, A/B testing, predictive & prescriptive modelling - Model deployment, monitoring, CI/CD, versioning - Solution Design, Scalable, reliable, and production ready model architectures - Leadership & Mentoring. - Decision Making.

Align data science initiatives with business goals and identify high-impact use cases. Actively design, build, and review models (ML, forecasting, optimization), perform complex analyses, and validate results. Lead development of machine learning, forecasting, and optimization solutions. Ensure data governance, integrity, and compliance standards. Implement MLOps practices for deployment, monitoring, and scalability. Translate business needs into analytical solutions and communicate insights clearly. Prioritize projects, track KPIs, and ensure measurable business impact. Promote experimentation, new technologies, and continuous improvement.