The Data Scientist is responsible for driving the analytical, statistical, and programming interpretation of data to support decision-making and deliver measurable business results. This role provides product and business teams with insights derived from company and customer data, enabling predictions, proposals, and recommendations to improve business outcomes.

The Data Scientist leverages internal and external datasets using best-practice methodologies and advanced AI techniques. The role drives measurable business outcomes by transforming data into automated decisions and building tools that trigger automated actions. The incumbent is also accountable for developing next-generation foundational data infrastructure and delivering actionable insights that generate value.

Key Performance Areas (KPAs)

Strategy Development and Implementation

• Lead and provide expertise on big data and AI initiatives, analyzing business requirements, designing models, and ensuring alignment with application architecture.

• Translate business strategy into AI use cases that deliver measurable value (e.g., revenue growth, efficiency gains, risk reduction).

• Support implementation of data science strategies to enhance operational efficiency, optimize customer experience, and drive revenue growth.

• Develop comprehensive plans and timelines for executing data science initiatives, aligning with budget and resource availability.

• Provide reports and analyses to support functional strategy development.

• Ensure accurate and timely reporting for regular review of strategy, roadmap, and performance.

• Collaborate and communicate with technical, business, and partner teams with clarity and consistency.

Operational Delivery

• Support overall business strategy and goals through detailed data analysis, resulting in policies, programs, and initiatives that improve service delivery.

• Utilize advanced algorithms and analytics to manage large transactional datasets, optimize profitability, and support strategic decision-making.

• Produce intelligent, scalable, and automated solutions using Data Science skills.

• Perform complex analyses, including optimization, text analytics, machine learning, social-science modeling, and statistical analysis.

• Work with technology teams to develop new data capabilities and architecture.

• Develop and deploy advanced models and algorithms for accurate risk assessment.

• Organize and monitor pilot programs to test models before full-scale implementation.

• Deliver actionable insights through big data analytics, informing management strategies.

• Ensure models are production-ready with monitoring, retraining, and lifecycle management.

• Integrate AI with existing data, BI, and operational platforms.

• Develop and optimize predictive capabilities and digital banking analytics.

Governance

• Provide input in strategic, operational, and tactical meetings.

• Implement risk mitigation and controls; provide input on change initiatives, policies, and procedures.

• Implement responsible AI practices: fairness, transparency, explainability, bias detection.

• Align with regulatory and ethical guidelines (data privacy, industry-specific AI rules).

• Establish governance processes: model approvals, audit trails, monitoring dashboards.

Escalations

• Escalate critical issues affecting time, scope, productivity, cost, or resources.

• Provide solutions for escalations impacting multiple processes/functions.

Function Tactical

• Execute day-to-day operations and resolve operational issues within specialization.

• Contribute to objectives, targets, and budgets for the function.

• Identify and document risks, issues, and dependencies; define mitigation actions.

• Prepare documentation for sign-off on tactical decisions.

Performance

• Consistently execute work against goals and targets aligned with business objectives.

• Maintain strong relationships with stakeholders for sustained performance.

• Monitor performance alignment with the data science strategy.

• Drive experimentation through POCs with smooth transition to scaled deployments.

Reporting

• Provide periodic and ad-hoc reports on function progress and projects.

Job Requirements

Education

• Minimum 4-year degree in Computer Science, Mathematics, Statistics, Data Science, or related field.

• Master’s degree in Data Science, AI/ML, Statistics, or related field preferred.

• MBA or additional relevant Master’s advantageous.

Experience

• 5+ years in relevant roles (Data Engineer / Data Scientist).

• 4–6+ years applied data science experience; 2+ years owning production AI/ML.

• 3+ years in fintech, banking, or financial services sector.

• Proven delivery of automated decisioning (recommendation, propensity, fraud, forecasting) with measurable business impact.

• Experience in data engineering for enterprise-scale infrastructures.

• Agile/DevOps experience; GitHub/GitLab for CI/CD.

• Python (and libraries), SAS, R, Scala for data cleaning and advanced analytics.

• Knowledge of Hadoop, Apache Spark, MapReduce, Hive, PIG.

• Experience with BI tools: Power BI, Data Studio, Kibana, Tableau.

• Proficient with RDBMS and Big Data platforms (Postgres, Oracle, MongoDB, MSSQL).

• Experience in ecommerce, electronic payments, or fintech platforms advantageous.

• Global exposure with understanding of political, social, infrastructure, and integrity challenges.

Competencies

Functional Knowledge

• Data Engineering, Mining, and Analytics

• Machine Learning and Predictive Modeling

• AI/ML implementation for predictive use cases

• Payment, e-commerce, and digital platform analytics

• Data Warehousing, RDBMS, and Big Data management

Skills

• Digital marketing and analytics

• Strategic thinking and decision making

• Organizational agility and digital mindset

• Dealing with ambiguity and complexity

• Project and people management

• Executive-level presentation

Behavioural Qualities

• Analytical, detail-oriented, and methodical

• Operationally astute and proactive

• Organized and driven