Data Scientist-I-SUPPORT SERVICES-CTO Head Kotak Mahindra Bank Limited

2.0 - 5.0 years

0 Lacs

bengaluru, karnataka, india

On-site

About Kotak Mahindra Group: Established in 1985, the Kotak Mahindra Group is one of India's leading financial services conglomerates. In February 2003, Kotak Mahindra Finance Ltd (KMFL), the group's flagship company, received a banking license from the Reserve Bank of India (RBI). With this, KMFL became the first non-banking finance company in India to become a bank - Kotak Mahindra Bank Limited. The Group offers a wide range of financial services that encompass every sphere of life. From commercial banking, to stock broking, mutual funds, life insurance and investment banking, the Group caters to the diverse financial needs of individuals and the corporate sector. The Group has a wide distribution network through branches and franchisees across India, the international offices in London, New York, California, Dubai, Abu Dhabi, Bahrain, Mauritius and Singapore. For information, please visit the company's website at Job Description: The Retail Assets Data Science team in Kotak Mahindra Bank is responsible for developing risk, marketing and allied scorecards for Retail Assets products. The core work charter includes supporting different products under Retail assets by developing marketing, cross-selling, acquisition, customer management and collections scorecards for multiple products (credit cards, personal loans, consumer durables etc.) using cutting-edge Data Science capabilities and Data platforms. As part of the data science function for Retail Assets business, one should be at the forefront of a data-driven initiative to optimize decision making.The Data Scientist role will draw upon knowledge of programming and Quantitative solutions and be an integral part in the design, development and maintenance of effective and compliant statistical risk and decision support models and related analytics. This role focuses on leveraging data science techniques for model development, driving automation and process improvement while demonstrating innovative thinking, passion for data science and fostering cross-team collaboration. Establishing strong working relationship with stakeholders in the bank is also a key aspect of this role. Roles and Responsibilities: Develop and deploy machine learning models for risk, marketing and other essential metrics, collaborating closely with stakeholders to ensure accurate and timely implementation. Carry out data processing including statistical analysis, variable selection, and dimensionality reduction, custom attribute engineering, as well as the evaluation of new data sources. Use ML techniques such as regression, classification, clustering, tree-based algorithms, support vector machine to address business problems. Improve upon existing methodologies by developing new data sources/features, testing model enhancements, and fine-tuning model parameters. Provide the business and other stakeholders with advice and support on model use, model impact and model implementation. Work with model governance teams to develop methods and metrics for ensuring all models continue to perform as expected according to internal and external model risk standards. Understand the systems and the business processes that populate those systems with data Automate processes to ensure scalability of solutions by collaborating with cross-functional teams. Skill requirements: Bachelors/Masters in a quantitative field (Engineering, Statistics, Econometrics, Mathematics, Computer Science etc) 2-5 years of experience as a Data Scientist preferably in the banking or financial services sectors Strong programming background (Python, Pyspark, SAS, SQL etc.) Hands on experience in building and implementing best in class Machine Learning models. Familiarity with ML models - XGBoost, Light GBM, Random Forests, SVMs, multivariate analysis, clustering, dimensionality Experience in carrying out data processing including statistical analysis, variable selection, and dimensionality reduction, custom feature engineering, as well as the evaluation of new alternate data sources Experience in partnering with technology teams to implement models/algorithms in production Ability to communicate clearly and effectively with stakeholders at all levels of thebusiness Ability to work independently and deal with ambiguity. Highly motivated and collaborative team player with strong interpersonal skills

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