Key Responsibilities:
- Collaborate with stakeholders across business units to identify data-driven opportunities and develop innovative business solutions
- Analyze complex datasets using advanced statistical methods to uncover hidden patterns and generate actionable insights
- Design and develop customized machine learning and AI algorithms tailored to specific customer requirements
- Create scalable, reusable AI/ML/statistical models that can be deployed across multiple business units with minimal customization
- Translate analytical findings into clear, actionable recommendations for business and product improvement
- Partner with product and engineering teams throughout the project lifecycle to ensure data requirements are well-defined and properly captured
- Lead product and project initiatives independently while providing technical guidance and mentorship to team members
- Develop and maintain interactive dashboards using tools such as Power BI to communicate algorithm results and insights to stakeholders
- Communicate complex analytical concepts effectively to audiences at all organizational levels through clear, data-driven storytelling
Technical Responsibilities:
- Perform comprehensive data exploration, preprocessing, data transformation, validation, reconciliation, feature engineering, and quality assurance using Python
- Apply analytical and advanced machine learning algorithms including regression, classification, clustering, support vector machines, decision trees, random forests, and neural networks
- Implement specialized analytics techniques such as design of experiments, forecasting, segmentation, and uncertainty analysis
- Develop and deploy APIs and web applications using Flask or Django frameworks
- Deploy models to cloud platforms such as Azure or AWS with containerization using Docker and CI/CD pipelines
- Develop efficient and maintainable Python solutions for processing, transforming, validating, and reconciling large datasets, including handling millions of records
- Implement deterministic business logic, configurable validation rules, error handling, and data-quality checks for complex real-world datasets
Qualifications
B.E / B.Tech or equivalent technical qualification
Strong programming fundamentals in Python and experience working with structured and large-scale datasets.
Additional Information
3 to 6 years
B.E
3 to 6 years