Data Scientist -Machine learning

Job description

Technology-\>AI-Data science-\>Machine Learning,Technology-\>AI-Data science-\>PYTHON 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. Minimum Qualifications: * UG education in Computers: BTECH / BSC / BCA (Computers must be included in UG). * 5--8 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. * 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.