Role Objective:

Drive data-driven business decision-making by transforming complex customer, employee, and business data into actionable insights, predictive analytics, and strategic recommendations that improve performance, optimize outcomes, and deliver measurable business impact.

Functional Responsibilities:

Business Problem Solving & Analytics:

- Understand business objectives and translate ambiguous business questions into structured analytical problems.

- Identify the appropriate datasets, analytical methodologies and KPIs required to answer business questions.

- Perform exploratory and diagnostic analysis to identify trends, patterns, relationships, anomalies and key drivers.

- Develop analytical frameworks to evaluate business performance and support decision-making.

- Work on projects where outcomes may depend on complex and less predictable human-customer behaviour.

Customer / People Analytics:

- Analyze customer, consumer, employee or other people-related datasets to identify behavioural patterns and actionable segments.

- Conduct segmentation, profiling, propensity and behavioural analysis where relevant.

- Identify factors influencing acquisition, conversion, retention, engagement or other business outcomes.

- Develop analytical approaches for understanding heterogeneous populations rather than relying only on aggregate-level metrics.

- Evaluate the effectiveness of different interventions, campaigns, channels or strategies on people-related outcomes.

- Apply statistical techniques to determine whether observed differences or relationships are meaningful.

Quantitative & Statistical Analysis:

- Conduct statistical analysis using appropriate methodologies depending on the business problem.

- Apply techniques such as:

1. Regression analysis

2. Hypothesis testing

3. A/B testing-experimentation

4. Correlation and driver analysis

5. Segmentation

6. Forecasting-time-series analysis

7. Classification and propensity analysis

8. Predictive modelling where appropriate

- Validate analytical results and assess robustness of findings.

- Distinguish correlation from meaningful business relationships and avoid drawing unsupported conclusions.

- Quantify the potential impact of recommendations wherever possible.

Data Preparation & Analysis:

- Extract, clean, transform and structure data from multiple sources.

- Work with large and potentially messy datasets.

- Perform data-quality checks and investigate inconsistencies before analysis.

- Use SQL and Python-R or equivalent analytical programming tools for data manipulation and analysis.

- Build repeatable analytical workflows rather than relying solely on manual spreadsheet-based analysis.

- Work with structured and unstructured data where required.

Insight Generation & Business Recommendations:

- Convert analytical outputs into concise, business-oriented insights.

- Develop data-backed recommendations for improving business performance.

- Quantify potential benefits, risks and trade-offs associated with recommendations where possible.

- Support stakeholders in evaluating alternative business strategies using data.

- Should be able to connect analytical work with measurable business outcomes.

Visualization & Communication:

- Develop clear and effective dashboards, charts and analytical presentations.

- Use visualization to communicate patterns and insights rather than simply displaying data.

- Present findings to non-technical stakeholders in a simple, structured manner.

- Explain analytical methodologies and findings without unnecessary technical complexity.

Stakeholder & Project Collaboration:

- Work closely with business, research, strategy and other functional teams to understand requirements.

- Clarify ambiguous requirements and challenge assumptions where necessary.

- Manage analytical projects from problem definition through analysis, interpretation and recommendation.

- Communicate project progress, findings and limitations effectively.

- Collaborate with technical and non-technical stakeholders.

Technical Skills Expected:

- SQL

- Python

- Data Cleaning & Visualization

- Predictive Analytics

- Machine Learning

- Big Data & Cloud (Data Bricks, Pyspark, Cloud Analytics Platforms)