Data Scientist -Machine learning

Job description

Service Line Data Analytics Unit Responsibilities - Technical Delivery Modeling: Lead end-to-end data science and machine learning project execution from discovery to deployment-ready deliverables. - Design, develop, and evaluate ML models aligned to business objectives, ensuring robust performance and generalization. - Perform data exploration, feature engineering, and model selection to improve predictive accuracy and reliability. - Establish model validation approaches, track metrics, and document assumptions, limitations, and outcomes. - Consulting Stakeholder Management: Partner with stakeholders to translate business problems into analytical frameworks and measurable success criteria. - Communicate insights and model results clearly to technical and non-technical audiences, enabling decision-making. - Drive solution recommendations with a focus on feasibility, scalability, and business impact. - Leadership Quality: Provide technical guidance and mentorship to team members, promoting strong engineering and modeling practices. - Review code, experiments, and outputs to ensure quality, reproducibility, and maintainability. - Contribute to reusable assets, templates, and best practices for consistent delivery across initiatives. Qualifications - Educational Requirements: Bachelor of Engineering, BTech, BCA. - UG education in Computers: BTech / BSC / BCA (Computers must be included in UG). - 58 years of experience in Data Science, Machine Learning, and AI/ML solution delivery. - Strong hands-on experience with Python for data science workflows and model development. - Proven ability to build, evaluate, and improve ML models using sound statistical and analytical techniques. - Experience working with stakeholders to define problem statements, success metrics, and actionable outcomes. Additional Responsibilities - Experience leading teams or workstreams, including mentoring, technical reviews, and delivery ownership. - Strong proficiency with Python data science ecosystem (e.g., NumPy, Pandas, scikit-learn) and experiment tracking practices. - Exposure to deep learning or advanced ML techniques and frameworks (e.g., TensorFlow, PyTorch) where applicable. - Ability to design scalable solution approaches and collaborate effectively in a hybrid work environment. - Strong documentation and communication skills to present insights, trade-offs, and recommendations with clarity. Technical and Professional Requirements - Technology- >AI-Data science- >Machine Learning - Technology- >AI-Data science- >PYTHON Preferred Skills