Data Scientist -Machine learning

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. Minimum Qualifications:

UG education in Computers: BTECH / BSC / BCA (Computers must be included in UG).

5â€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.

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