Data Scientist

About the Digital Business Unit at IndusInd: The mandate of the Digital Business Unit at IndusInd Bank is as follows:

• Building customer centric products with human centered design principles for retail Individual and micro, small and medium enterprise (MSME) customer segments
• Build innovative products and propositions backed with problem solving mindset to discover and solve latent needs of customers
• Build Embedded Finance (Banking as a Service) applications
• Ensure designs are highly available, highly modular, highly scalable and highly secure
• Drive digital business

Some of the applications managed by the Digital Business Unit include IndusMobile (bank’s mobile app for retail individual clients), Indusnet (net banking application of the bank), IndusMerchantSolutions app, Whatsapp Banking, Chatbots, easycredit for Individuals, easycredit for Business Owners, savings, current account and fixed deposit online platforms. There are many more innovative digital products and solutions in pipeline The unit’s objectives are three fold – (a) Drive better customer experience and engagement (b) transform existing lines of businesses and (c) build new digital only or banking as a service led digital business models About the Role: You would be part of asset analytics and data science team and work on cutting edge problems for the bank. The individual will work closely with the stakeholders across risk, business, partnerships, digital and strategy in creating and refining strategies to augment profitability and growth for the bank. The incumbent will majorly be responsible with coming up data driven and actionable insights and presenting them to relevant stakeholders The candidate will work in close collaboration with digital product, growth, and marketing teams. Job Responsibilities:

• Experience querying databases and using statistical computer languages: R, Python, SLQ, etc.
• Use predictive modelling to increase and optimize customer experiences, revenue generation, ad targeting and other business outcomes.
• Experienced in working with large and multiple datasets, data warehouses and ability to pull data using relevant programs and coding.
• Well versed with necessary data reprocessing and feature engineering skills.
• Strong background in Statistical Analysis. Constantly look and research on ML algorithms and data sources for better prediction
• Work and coordinate with multiple stakeholders to identify opportunities for leveraging company data to drive business solutions, implement models and monitor outcomes.
• Assess the effectiveness and accuracy of new data sources and data gathering techniques and develop processes and tools to monitor and analyze model performance and data accuracy.
• Experience in establishing/scaling up data science functions
• Proven ability to discover solutions hidden in large datasets and to drive business results with their data-based insights
• Leverage analytics to increase customer lifetime value for clients acquired digitally by pitching right product to the right client at the right time
• Help define pricing models for digital value propositions for various segments of users / clients to ensure profitability of the portfolio and to ensure achievement of business outcomes
• Work with product, growth, and marketing teams across product/campaign lifecycle
• Empower product and marketing teams by creating automated dashboards and reports using PowerBI

Education, Work Experience, Key Skill Set Requirements:

• BE / B-Tech/M.E./M.Tech/MBA/PGDBM from Tier 1/Tier 2 colleges
• 2-4 years of Data Science experience in Banking/ Financial Services
• Model development experience in Python
• Strong and in-depth understanding of statistics
• Strong strategic thought leadership and problem-solving skills with ability to tackle unstructured and complex business problems
• Excellent written and verbal communication
• Ability to build & use relationships and influence broadly across the organization
• Results driven with strong project management skills, ability to work on multiple priorities
• Handling Big Data, Segmentation, Analytics, Machine Learning, Artificial Intelligence, Statistics and Hypothesis Testing