Data Scientist

The Data Scientist will lead the development of AI/ML solutions that drive data‑driven decision‑making across customer engagement, marketing, retention, risk, and product functions. The role requires strong analytical expertise, hands‑on experience with open‑source technologies, and the ability to build interactive applications and deploy models in both batch and real‑time environments. The candidate will ensure high‑quality delivery, effective stakeholder communication, and adherence to best practices for reproducible, scalable data science. What You Will Do: Work with Data Science team to translate given business problem into analytical use-cases with defined outcomes to develop, implement and test most appropriate algorithms for a given use-case. Work closely with Business Analyst for requirement gathering/understanding and work with Data Engineers to build data pipelines and automate/production Alize complex ML models to insights and recommendations. Strong conceptual understanding of machine learning algorithms including multi-variate regressions, classification algorithms, time series techniques, clustering, NLP, Image Processing, and optimization models etc. Ensure high coding standards as well as designing standards to ensure reproducibility. Drive innovation by enhancing existing solutions and designing new ones and build collaboration and awareness in the bank’s analytics community Work closely with various business units/stakeholders to identify and streamline the AI-ML use-cases. Deliver end-to-end AI-ML models from development to deployment with delivery planning, communications with stakeholders, and ensuring efficient usage of the models by the business as recommendations for improving decision touchpoints. Ensure high coding standards, peer-review, and transparency in the work with the line of reporting. Support ad-hoc requirement in terms of MIS development, building and analyzing SAS Data Models, streamlining the reporting process through various reporting and analytical tools. What We Are Looking For: Overall 9+ years of experience in analytics, data science or similar function 3-4 years of experience in banking analytics Python and SQL experience required Worked on end-to-end ML model deployment Machine Learning / Deep Learning / Time-Series / Optimization and Customer Analytics Technical Skills: Strong coding skills using Python. Knowledge on Retail Banking Products Deployment experience (various databases, server/cloud environment: AWS, Azure, and APIs, ODBCs, web apps) Excellent knowledge of Banking Functional Knowledge (major plus) Good written, oral communication, documentation skills with ability to communicate effectively with stakeholders. Expert-level proficiency in

Work with Data Science team to translate given business problem into analytical use-cases with defined outcomes to develop, implement and test most appropriate algorithms for a given use-case. Work closely with Business Analyst for requirement gathering/understanding and work with Data Engineers to build data pipelines and automate/production Alize complex ML models to insights and recommendations. Strong conceptual understanding of machine learning algorithms including multi-variate regressions, classification algorithms, time series techniques, clustering, NLP, Image Processing, and optimization models etc. Ensure high coding standards as well as designing standards to ensure reproducibility. Drive innovation by enhancing existing solutions and designing new ones and build collaboration and awareness in the bank’s analytics community. Work closely with various business units/stakeholders to identify and streamline the AI-ML use-cases. Deliver end-to-end AI-ML models from development to deployment with delivery planning, communications with stakeholders, and ensuring efficient usage of the models by the business as recommendations for improving decision touchpoints. Ensure high coding standards, peer-review, and transparency in the work with the line of reporting. Support ad-hoc requirement in terms of MIS development, building and analyzing SAS Data Models, streamlining the reporting process through various reporting and analytical tools.