Digital Data Scientist

Key Performance Indicators

Advanced Analytics & AI Enablement

• Lead the development of predictive, prescriptive, and ML models for growth, risk, customer experience, and efficiency.
• Build reusable data products, dashboards, and analytical APIs for business self-service.
• Deploy AI/ML use cases across domains such as Cards, PL, ML, SME, Deposits, Digital Channels, Collections, and Marketing .
• Ensure measurable business impact (revenue uplift, cost reduction, risk mitigation).

Business Partnering & Value Realisation

• Partner with senior business stakeholders to identify high-value data opportunities .
• Translate business problems into analytical solutions with clear ROI and success metrics .
• Challenge assumptions with data-driven insights and influence strategic decisions.

Data Products & Platform Adoption

• Promote scalable analytics using cloud, big data, and modern ML platforms.
• Improve analytics maturity across teams through enablement and best practices.

Data Strategy & Digital Data Journey

• Define and execute the enterprise data & analytics roadmap aligned with business strategy.
• Establish data as a single source of truth through governed, scalable, and reusable data assets.
• Drive adoption of advanced analytics, AI, and automation across business units.

Job Responsibilities

• Lead the design, development, and deployment of advanced analytics and AI solutions.
• Own the end-to-end analytics lifecycle : problem framing, data preparation, modelling, validation, deployment, and monitoring.
• Guide teams on feature engineering, model selection, performance tuning, and interpretability .
• Drive data storytelling and executive-level insights through compelling narratives and visuals.
• Mentor and develop data scientists and analysts; set technical and delivery standards.
• Collaborate with IT, Data Engineering, Risk, Finance, and Product teams to embed analytics into workflows.
• Prioritise initiatives using value vs effort and ensure timely execution.

Core Skills & Competencies

• Strong hands-on expertise in Python/R / SQL and modern data science frameworks.
• Proven experience in machine learning, statistical modelling, and AI techniques .
• Strong understanding of data architecture, data pipelines, and cloud analytics platforms .
• Ability to convert complex analytical outputs into clear business recommendations .
• Excellent stakeholder management, communication, and presentation skills.
• Strategic mindset with execution focus.
• Ability to manage multiple initiatives in a fast-paced environment.

Educational & Other Qualifications

• Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Engineering, or related field .
• 7–10 years of experience in data science, advanced analytics, or AI roles, preferably within banking or financial services .
• Strong exposure to banking/NBFC domains (Cards, Loans, SME, Digital Channels, Risk, Marketing).
• Experience working with cloud platforms (AWS, Azure, GCP) and big-data ecosystems.
• Exposure to data & risk models, and regulatory environments is a strong advantage.