Principal Data Scientist - Bank - IIT/NIT/IIM/ISB (10-20 yrs)

Principal Data Scientist

The Principal Data Scientist is tasked with championing data-driven decision-making and fostering a data-centric culture within the bank. This individual will lead the Data Analytics team, playing a pivotal role in leveraging data to uncover new opportunities and boost the bank's competitiveness in the digital arena.

Major Roles & Responsibilities:

- Develop and Implement Data Strategy: Craft and execute a comprehensive data strategy that aligns with the bank's goals. Work with stakeholders to identify strategic opportunities for data utilization and cross-selling.

- Design and Implement Data Management Systems: Oversee the creation and implementation of efficient and scalable data management systems and structures.

- Data Architecture and Integration: Define and maintain data architecture standards, principles, and models. Coordinate data integration initiatives, including data sourcing and consolidation efforts.

- Promote Data-Driven Culture: Encourage data-driven decision-making throughout the organization by fostering a data-centric culture.

- Advanced Analytics Development: Lead the development and implementation of advanced analytics capabilities, such as predictive modeling and data visualization.

- Collaborate with Business Units: Partner with various business units to identify and prioritize data-driven insights and initiatives that enhance customer experience, risk management, and revenue generation.

- Compliance and Regulation: Work closely with designated officers to ensure compliance with data protection, privacy, and security regulations, including guidelines from the Reserve Bank of India (RBI).

- Awareness and Advocacy: Promote the importance and value of data across the organization, advocating for the effective use of data and analytics to enhance business processes and decision-making.

- Data Literacy and Training: Establish programs to build data literacy and competency within the bank.

- Change Management: Lead change management initiatives to ensure the successful adoption of data-driven practices and technologies.

Skills Required:

- Machine Learning Algorithms: Proficiency in algorithms such as Linear Regression, Logistic Regression, Decision Trees, etc.

- Statistical Skills: Strong knowledge of statistical tests, data distributions, and other statistical concepts.

- Big Data Experience: Experience with big data tools like Apache Spark, SAS, and BigML.

- Data Mining Techniques: Proficiency in data mining tools and techniques, including Apache Mahout, H2O, and Oracle Data Mining.

- Data Modelling and Optimization: Knowledge of data modeling and optimization techniques.

- Data Frameworks: Experience with data frameworks like Hadoop, and database query languages such as SQL and Hive, and data wrangling tools like Talend.

- Banking Knowledge: Understanding of banking products and processes.

- Data Management and Governance: Strong grasp of data management practices, data architecture, data quality, and data governance frameworks.

- Data Analytics and Visualization Tools: Proficiency in analytics and visualization tools and data integration technologies.

- Regulatory Compliance: Familiarity with GoI and RBI guidelines and international data handling standards.

- Leadership and Communication: Excellent leadership and communication skills to collaborate with cross-functional teams and senior management.

Job Specification:

Qualifications: Graduate or Post Graduate degree in Computer Science, Data Management, Information Systems, Engineering, Mathematics, Statistics, Economics, Econometrics, or a related field.

Experience: At least 10 years of experience in data management, including roles in data governance, data architecture, or data analytics, preferably within banks, financial institutions, BFSI, or fintech sectors. A minimum of 3 years should be in a mid-level managerial position.

Desirable Additional Qualifications/Experience:

- Expertise in Machine Learning, Natural Language Processing, Generative AI, Chatbots, etc.

- Proficiency in the latest analytical and data visualization tools such as Apache Superset, Tableau, R, Power BI, and Python.

- Experience with relational databases like Oracle and PostgreSQL, and non-relational databases like MongoDB.

- Familiarity with open-source AI/ML tools like Llama, Langchain, and Huggingface.

- Experience with RESTful APIs and general SOA architecture.

- Functional knowledge of ML techniques and tools like TensorFlow, Keras, and Apache MxNet would be an advantage.