Data Scientist -Machine learning

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.