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.

Technology->AI-Data science->Machine Learning,Technology->AI-Data science->PYTHON

• 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.