The role of the Manager: Data Analytics is to automate data pipelines, structure datasets, and leverage internal and external data to build and evolve the organization’s services and product capabilities using best-practice methodologies and statistical techniques. The incumbent will structure datasets at both local and group levels, design and provide insights based on analytics, deliver necessary reporting, and develop decision models and data-based rules to support the strategy and plans of the product portfolio.

The Manager will also support product and services strategies, ensuring prioritization and standardization of deliverables, and contribute towards operational performance across product portfolios and local operations.

Key Performance Areas (KPAs)

Strategy Development and Implementation

• Support analytics strategy execution in line with business and functional goals.

• Provide reports and analyses to support functional strategy development.

• Support execution and development of data mining and analysis frameworks.

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

Operational Delivery

• Drive analytics and reporting roadmap; prioritize business insight requests.

• Develop and manage models to ensure business and customer growth.

• Develop models for machine learning and data science applications.

• Build advanced quantitative modules using various software to support predictive assessment.

• Automate processes for data ingestion, transformation, and visualization.

• Create BI content: dashboards, reports, scorecards, and analytic solutions.

• Perform complex analyses: optimization, text analytics, machine learning, statistical modeling.

• Collate business intelligence data from public, industry, and purchased sources.

• Establish reporting methodologies and conduct ad-hoc market analysis.

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

• Develop and maintain frameworks to organize and make data available for analytics.

• Define and optimize targeting approaches for products and services.

• Develop predictive models for transaction volumes, patterns, and other business metrics.

• Implement ML techniques to enhance fraud detection and risk management.

• Monitor data for compliance and provide feedback for mitigation strategies.

• Identify operational bottlenecks and recommend data-driven improvements.

• Implement A/B testing and experimentation to validate enhancements.

• Support development of product specifications, scope of work, and data management guidelines.

• Enable implementation of key data analytics initiatives across operations.

• Track platforms using contextual dashboards for teams and the organization.

• Participate in project status meetings and sprints, providing analytics and decision support.

• Implement best-practice pipeline frameworks (KDD, CRISP) for product use cases.

• Manage tag management implementations and collaborate with Data Architecture/Engineering teams.

• Prepare executive presentations for leadership forums.

Governance

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

• Contribute to risk mitigation and control strategies.

• Support proposals for change initiatives, policies, and procedures.

Escalations

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

• Provide solutions for escalations impacting multiple functions.

Reporting

• Report periodically on function progress against metrics.

• Provide ad-hoc reports for specific projects.

Performance

• Execute work consistently against goals and targets, aligned with business objectives.

• Maintain strong relationships with stakeholders for sustained performance.

Job Requirements

Education

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

• Master’s in Business Administration is advantageous.

Experience

• Minimum 5 years in financial services, consulting, strategy, analytics, engineering, or related fields.

• 2–3 years in data structuration, management, analytics, or big data environments.

• 1–2 years in Data Science at a specialist level within finance, banking, or telecommunications.

• Experience in medium to large organizations.

• Agile/DevOps environment experience.

• Proficiency in Python (NumPy, Pandas, Scikit-learn, TensorFlow, Keras), SAS, R, or Scala.

• Knowledge of Hadoop, Apache Spark, and related Big Data technologies.

• Experience with data visualization and reporting tools: Tableau, Power BI, D3, VBA, SQL, Business Objects.

• Proficiency in databases: Postgres, Oracle, MongoDB, MSSQL.

• Experience across global regions with understanding of political, social, infrastructure, and integrity challenges.

• Cloud technology experience, preferably Azure.

Competencies

Functional Knowledge

• Data Science & Analytics

• Data Mining & Modeling

• Machine Learning & Predictive Modeling

• Data Management Systems

• Data Visualization and Dashboards

• Advanced Analytics and Big Data

• Statistical & Quantitative Analytics

• Forecasting / Predictive Analytics

• Business Intelligence Systems and Tools

• Data Science Toolkits (SAS, R, SPSS)

Skills

• Strong analytics, data interpretation, and presentation skills

• Financial and numerical acumen

• Analytical thinking and problem-solving

• Continuous improvement mindset

• Ability to work with ambiguity and complexity

• Delivery-focused with digital mindset

• Organizational agility and effective presentation skills

Behavioural Qualities

• Adaptable and culturally aware

• Emotionally mature

• Integrity, innovation, and leadership

• Team player and collaborative approach