Job Purpose

The Manager – Data Analytics is responsible for automating data pipelines, structuring and managing datasets, and leveraging internal and external data sources to enhance business products, services, and decision-making capabilities using industry best practices, statistical techniques, and machine learning methodologies.

The role is accountable for designing scalable data models, generating actionable business insights, developing reporting frameworks, and implementing data-driven decision models that support business strategy, product development, operational excellence, and organizational growth. The incumbent also supports the execution of the data analytics strategy while ensuring the prioritization, standardization, and successful delivery of analytics initiatives across the organization.

Key Responsibilities

Strategy Development & Execution

• Support the implementation of the organization’s data analytics strategy aligned with business objectives.
• Provide reports, dashboards, and analytical insights to support strategic planning and decision-making.
• Develop and enhance data mining and analytics frameworks.
• Ensure timely, accurate, and reliable reporting to monitor business performance and strategic initiatives.
• Identify opportunities to improve business performance through data-driven recommendations.

Data Analytics & Business Intelligence

• Drive the analytics and reporting roadmap while balancing tactical and strategic business needs.
• Develop and maintain predictive and analytical models that support customer and business growth.
• Build machine learning and data science models for advanced analytics and intelligent decision-making.
• Develop quantitative models to support forecasting and predictive analysis.
• Automate data ingestion, transformation, validation, and visualization processes.
• Design and develop Business Intelligence (BI) reports, dashboards, scorecards, and analytical solutions.
• Perform advanced statistical analysis, optimization, predictive analytics, text analytics, and machine learning.
• Collect and analyze internal and external data sources to identify market trends and business opportunities.
• Conduct ad-hoc analysis and develop reporting methodologies to support business objectives.
• Collaborate with technology teams to define data architecture requirements and improve analytics capabilities.
• Design frameworks to organize, structure, and maintain data assets for business use.
• Develop predictive models for transaction forecasting, customer behavior, demand planning, and operational performance.
• Implement machine learning techniques to enhance fraud detection, risk management, and operational efficiency.
• Monitor data quality and regulatory compliance while supporting risk mitigation initiatives.
• Identify operational bottlenecks and recommend data-driven process improvements.
• Design and execute A/B testing and experimentation to validate business improvements.
• Support product development through data analysis, product specifications, and business requirements documentation.
• Analyze business performance and recommend initiatives to improve productivity and efficiency.
• Support business units in implementing enterprise-wide analytics initiatives.
• Collaborate with cross-functional teams including Technology, Finance, Marketing, Operations, Risk, and Product Management to maximize data utilization.
• Develop interactive dashboards and visualization platforms for business stakeholders.
• Participate in project planning, agile ceremonies, and sprint reviews.
• Provide analytical decision support for strategic and operational initiatives.
• Implement best-practice data science methodologies such as KDD and CRISP-DM.
• Manage tag management implementations and digital analytics solutions.
• Collaborate with Data Engineering and Data Architecture teams to enhance enterprise data capabilities.
• Ensure service level agreements (SLAs) and turnaround times (TATs) are consistently achieved.
• Prepare executive-level presentations and reports for senior leadership and management committees.

Governance & Risk Management

• Provide analytical input into strategic and operational planning meetings.
• Support the development of policies, procedures, and governance frameworks related to data analytics.
• Identify business risks and recommend appropriate mitigation strategies.
• Escalate issues that may significantly impact project scope, timelines, resources, or costs.
• Support organizational change initiatives through data-driven recommendations.

Reporting & Performance Management

• Produce regular and ad-hoc reports for management and stakeholders.
• Monitor key performance indicators and business metrics.
• Ensure consistent execution of deliverables aligned with organizational goals.
• Build strong working relationships with internal stakeholders to promote collaboration and successful project delivery.

Qualifications

Education

• Bachelor’s Degree in Data Science, Statistics, Mathematics, Computer Science, Business Analytics, or a related discipline.
• Master’s Degree or MBA is considered an advantage.

Experience

• Minimum of 5 years of experience in data analytics, business intelligence, consulting, financial services, technology, engineering, or a related industry.
• 2–3 years of experience in data management, data engineering, analytics, big data, or similar environments.
• Experience in Data Science, predictive analytics, or machine learning.
• Experience working within medium to large organizations.
• Experience working in Agile and/or DevOps environments.

Technical Skills

• Strong proficiency in Python (NumPy, Pandas, SciPy, Scikit-learn, TensorFlow, Keras, Matplotlib, Seaborn, Bokeh) or R/Scala.
• Experience with SAS for advanced analytics is advantageous.
• Knowledge of Hadoop, Apache Spark, MapReduce, Hive, Pig, and other Big Data technologies.
• Strong SQL skills and experience with Business Intelligence platforms including Power BI, Tableau, Business Objects, and similar reporting tools.
• Experience with relational and NoSQL databases including PostgreSQL, Oracle, Microsoft SQL Server, and MongoDB.
• Knowledge of data warehousing, ETL processes, and cloud-based analytics platforms is desirable.

Core Competencies

• Data Analytics & Business Intelligence
• Machine Learning & Predictive Analytics
• Statistical Modeling
• Data Engineering & Automation
• Business Intelligence Reporting
• Data Visualization
• SQL & Database Management
• Python/R Programming
• Big Data Technologies
• Problem Solving & Critical Thinking
• Strategic Planning
• Stakeholder Management
• Project Management
• Agile Methodologies
• Communication & Presentation Skills
• Risk Analysis & Governance
• Cross-functional Collaboration
• Continuous Improvement