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.

- Solid 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

📌 Data Scientist -Machine learning (Bengaluru)
🏢 Infosys
📍 Bengaluru