Job Description

Job Purpose:

This portfolio of work will deliver data driven decisions across all critical business functions such as customer engagement, marketing, retention, risk management, products and tackle hard, open-ended problems. Data Scientist is a critical role, responsible for AI-ML solution design and development. The role requires a data science enthusiast with strong analytical expertise, experience of various open source technologies such as Python (must), R (plus), Spark (plus), and SAS (plus), algorithms and big data technologies to design approaches and build analytical solutions across various business domains. Along with it, experience in building interactive apps on Flask, Django, or RShiny (a major plus), experience in batch and real-time production deployment (must), and efficient in delivery planning, execution, communication with stakeholders, and maintaining best practices for high reproducibility.

Reports directly to: Data Science Lead - Business Performance & Strategic Analytics

Reports indirectly to: Director - Business Performance & Strategic Analytics

Key Responsibilities / Accountabilities

Functional:

• Work with Data Science Lead 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/productionalize 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 decisioning 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