Malabar Group - Senior Data Analyst

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)