Data Analyst | Government Project

Role & responsibilities

The Data Analyst(s) shall provide dedicated, in-situ data analytics support to the Directorate for analysis of policy, operational, claims, transaction and customer service data and for generation of actionable management insights. The scope shall include, but not be limited to, the following:

Data Requirement and Data Extraction

• Translate business and management requirements into structured data requirements, including relevant data fields, dimensions, measures, filters and reporting frequency.
• Coordinate with CEPT and concerned technical/data teams for obtaining the required authorized Projects data.
• Undertake data querying/extraction and prepare datasets required for recurring and adhoc analysis.

Data Preparation, Validation and Quality Checks

• Clean, structure and validate datasets before analysis.
• Undertake data-quality checks to identify missing, duplicate, inconsistent or anomalous records.
• Identify and communicate data-quality issues affecting the reliability or interpretation of analytical outputs.

Management Reporting and Dashboarding

• Prepare periodic management reports and scorecards for parameters identified by the Directorate.
• Develop dashboards and visualisations for monitoring key business and risk indicators.
• Prepare concise PowerPoint presentations and management briefs highlighting significant trends, exceptions, concentrations and areas requiring attention.

Policy and Business Analytics

• Analyze policy issuance/new business trends and relevant policy characteristics.
• Analyze premium payment behavior, lapsation, revival and persistency.
• Analyze surrender and other relevant policy/transaction patterns.
• Analyze agent performance and identify significant trends, concentrations and exceptions.

Claims and Early Death Claims Analytics

• Analyze claim pendency and claim settlement timelines.
• Identify concentration of death and early-death claims at Circle, Region, Division, CPC/HO and other appropriate levels.
• Identify unusual patterns or clusters requiring closer examination.
• Analyze available data relating to reasons for claim pendency and delays.

Risk Intelligence and Early-Warning Indicators

• Develop data-based indicators for identifying policies/transactions requiring closer examination.
• Identify unusual combinations or concentrations involving policy issuance, early death claims, lapsation, surrender, premium payment, policy relaxations and other relevant indicators.
• Develop early-warning indicators for preventive monitoring.
• Prepare risk/exception reports at Circle and other appropriate levels.
• Undertake analysis of policy characteristics and transaction patterns that may provide inputs for risk management and prevention/detection of possible mis-selling.

Customer Grievance Analytics

• Analyze public complaints to identify recurring or systemic issues.
• Identify geographical concentration and policy/service-related causes of complaints.
• Provide data-based inputs for improving customer service and reducing avoidable complaints.

Field-Level Analytical Support

• Provide actionable analytical inputs to Circles/Regions/Divisions/CPCs/Head Offices for corrective and preventive action.
• Identify areas requiring focused review, verification or investigation based on data patterns.
• Prepare focused analytical briefs/exception reports as required by the Directorate.

Ad-hoc Analytics and Management Support

• Provide end-to-end analytics support on priority requests, including data querying, analysis, insight generation and presentation.
• Undertake deep-dive analysis on any Project's parameter or business issue identified by the Directorate.
• Undertake any other related data analytics assignment entrusted by the designated authority.

Analytics Capability and Knowledge Transfer

• Develop repeatable analytical processes, reporting templates and reusable queries/models wherever practicable.
• Support training and knowledge transfer to designated Project personnel to strengthen internal data analytics capability.

Key Analytical Outputs/Deliverables:

• Monthly management scorecard covering relevant Project parameters, including agent activation, early death claims, lapsation & persistency and other parameters specified by the Directorate.
• Periodic deep-dive reports on claims, early death claims, policy issuance, premium payment, lapsation/revival, persistency, surrender and other priority areas.
• Circle-wise/appropriate-level risk and exception reports.
• Early-warning indicators and data-based exception indicators.
• Claims pendency and settlement-timeline analysis.
• Customer grievance trend and concentration analysis.
• Dashboards, visualizations and management presentations.
• Data-quality and validation observations relating to datasets used for analysis.
• Action-oriented analytical briefs for management and field formations.
• Reusable analytical templates/processes and knowledge-transfer material.

Desired Qualification and Skill Set:

• The Data Analyst should possess the following qualification, experience and technical/functional skills:
• Graduate/Post-Graduate qualification in Data Science, Statistics, Mathematics, Computer Science, Information Technology, Economics, Business Analytics, Engineering, Management or a related discipline.

Relevant Experience

• Relevant professional experience in data analytics, business analytics, MIS/management reporting, risk analytics or a related field, preferably involving large operational datasets.
• Advanced proficiency in Microsoft Excel, including modelling, pivot tables, dashboards, lookups, data validation and analytical functions.
• Strong proficiency in PowerPoint for preparation of concise management presentations and communication of analytical findings.
• Working knowledge of SQL for querying and extracting data from underlying source tables/databases.
• Working knowledge of ETL concepts/tools and data-quality validation processes.
• Working knowledge of dashboarding and business-intelligence tools such as Power BI or Tableau.
• Knowledge/proficiency in Python and/or R for data analysis, data manipulation and analytical modelling is desirable.
• Ability to perform trend analysis, exception analysis, concentration analysis, segmentation/clustering and other appropriate analytical technique
• Ability to understand business requirements, interpret insurance/operational metrics and convert data into clear, actionable insights.
• Ability to work effectively with business stakeholders, technical teams and data owners, including CEPT.
• Understanding of data confidentiality, access controls and secure handling of policyholder, claimant, agent and operational data.

Indicative Areas of PLI/RPLI Data Analytics

• New business and policy issuance

• Premium payment and transaction behaviour

• Lapsation, revival and persistency

• Surrender

• Death claims and early death claims

• Claim pendency and settlement timelines

• Agent activation and performance

• Policy characteristics and transaction patterns

• Policy relaxations and other relevant indicators

• Customer grievances/complaints

• Circle/Region/Division/CPC/HO-wise trends and concentrations

• Risk, exception and early-warning indicators.