Data Scientist

Role Overview and Responsibilities:

Business problem solving and use-case prioritisation: Partner with business and product stakeholders to translate business questions into structured analytics problem statements, define measurable outcomes, and prioritise high-value use cases. Apply knowledge of Retail Banking Liabilities, Investment and Insurance products, customer lifecycle, and channel behaviour.

Opportunity identification and insight generation: Analyse customer, transaction, product and engagement data to identify cross-sell, deepening, retention and activation opportunities. Generate actionable insights to improve relationship value, revenue per customer, primary-banker penetration and cross-sell ratio.

Hands-on analytics and data engineering: Independently explore, transform and analyse large-scale datasets using SQL, Python and PySpark. Build reusable analytical datasets, features and data pipelines; optimise Spark workloads; and apply sound coding, testing, documentation and version-control practices.

Pipeline orchestration and cloud delivery: Develop, schedule and monitor reliable batch workflows using Apache Airflow. Work with AWS services such as S3, EMR, Redshift, Glue and MWAA to support scalable analytical processing, data movement, orchestration and production deployment.

Analytics solution development: Build segmentation, propensity, classification, forecasting or rules-based solutions independently or in collaboration with data scientists. Validate outputs, explain methodology and limitations, and support implementation through campaigns, dashboards or downstream business processes.

Stakeholder management and communication: Translate analysis into clear business narratives, recommendations and decision-ready presentations. Work closely with Product, Business, Data Engineering, Data Science, Technology and Governance teams to resolve dependencies and drive implementation.

Data quality, governance and controls: Establish validation checks, reconcile source-to-output measures, troubleshoot pipeline failures, and ensure secure and compliant handling of customer data in line with internal policies and regulatory expectations.

Qualifications and Skills:

Education and experience: Bachelors or Master’s degree in Mathematics, Statistics, Economics, Computer Science, Technology, Engineering, Data Science or Management from Tier 1/ 2 institutes. Typically 5–10 years of relevant experience in business analytics, data analytics or data engineering, preferably in retail banking or financial services/ fintechs. 

Core technical skills: Strong hands-on proficiency in Python, SQL, PySpark and Apache Spark; experience building ETL/ELT pipelines and Apache Airflow DAGs; and practical exposure to AWS services including S3, EMR, Redshift, Glue and MWAA. Working knowledge of pandas, Jupyter, Git, data modelling, performance tuning and data-quality frameworks is expected.

Analytical capability: Strong grounding in exploratory analysis, statistical methods, customer segmentation, experimentation, feature engineering and model evaluation. Ability to convert ambiguous business questions into logical analytical frameworks and measurable recommendations.

Delivery and ownership: Manage assigned analytics initiatives from requirement clarification and data discovery through development, validation, implementation and post-production tracking. Proactively identify risks, communicate progress and deliver within agreed timelines.

Collaboration and mentoring: Operate as a senior individual contributor, review analytical work, share technical best practices, and mentor junior analysts where required. Demonstrate curiosity, structured problem solving, attention to detail and a strong bias for business impact.