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About the Role
Lead the development of AI/ML solutions to drive data-driven decision-making across customer engagement, marketing, retention, risk, and product functions. Ensure high-quality delivery, effective stakeholder communication, and adherence to best practices for reproducible, scalable data science.
Responsibilities
- Translate business problems into analytical use-cases with defined outcomes to develop, implement, and test appropriate algorithms.
- Collaborate with Business Analysts for requirement gathering and Data Engineers to build data pipelines.
- Automate and productionize complex ML models to generate insights and recommendations.
- Ensure high coding and design standards for reproducibility.
- Drive innovation by enhancing existing solutions and designing new ones.
- Build collaboration and awareness within the bank's analytics community.
- Identify and streamline AI-ML use-cases with various business units/stakeholders.
- Deliver end-to-end AI-ML models from development to deployment, including delivery planning and stakeholder communication.
- Ensure efficient usage of models by the business as recommendations for improving decision touchpoints.
- Ensure high coding standards, peer-review, and transparency in work.
- Support ad-hoc requirements including MIS development, SAS Data Model analysis, and reporting process streamlining.
Requirements
- Overall 7+ years of experience in analytics, data science, or similar function.
- 3-4 years of experience in banking analytics.
- Experience with end-to-end ML model deployment.
- Strong conceptual understanding of machine learning algorithms including multi-variate regressions, classification algorithms, time series techniques, clustering, NLP, Image Processing, and optimization models.
- Knowledge on Retail Banking Products.
- Excellent knowledge of Banking Functional Knowledge (major plus).
Skills
- Python
- SQL
- Machine Learning
- Deep Learning
- Time-Series Analysis
- Optimization
- Customer Analytics
- Deployment experience (various databases, server/cloud environment: AWS, Azure, and APIs, ODBCs, web apps)
- Written communication
- Oral communication
- Documentation skills
- Stakeholder communication
- SAS (plus)
- R (plus)
- Spark (plus)
Experience Level
- 7+ years of experience in analytics, data science or similar function
- 3-4 years of experience in banking analytics