Location Name: Pune Corporate Office - HO
Job Purpose
The Data Analytics & Insights Specialist will be part of the central Design Thinking Unit, responsible for transforming raw customer interaction, behavioral, and transactional data into validated insights and structured Signals that inform new product innovation. Should be proficient to leverage Data Science methodologies including statistical modeling, machine learning, and predictive analytics to enhance behavioral signal generation and innovation decision-making.
The role bridges data, generative research, and product strategy by:
- Designing data capture frameworks and analytical models
- Extracting and interpreting behavioral patterns
- Validating hypotheses emerging from qualitative and generative research
- Converting multi-source data into actionable innovation signals
The objective is not just reporting data — but building a behavioral understanding engine that enables evidence-led product thinking.
Duties And Responsibilities
- Behavioural & Journey Analytics (Governance-Led)
- Query and analyse large clickstream and behavioural datasets using SQL.
- Help decide what to track across customer journeys — what, where, and why.
- Work with engineering and analytics teams to keep tracking and pipelines aligned with measurement intent.
- Keep behavioural metrics consistent, interpretable, and tied to business questions.
- Use Databricks, CleverTap, Power BI, and Copilot Studio day-to-day.
- Perception & In-sighting
- Measure and analyse customer perception across journeys, features, and experiences.
- Integrate Voice of Customer metrics (NPS, CSAT, CES, feedback loops) with behavioural data.
- Conduct structured in-sighting to identify patterns in engagement, satisfaction, churn, and retention.
- Validate hypotheses emerging from generative research and customer Signals.
- Build cause–effect narratives linking experience design to business and customer outcomes.
- Translate complex datasets into clear, actionable insights influencing roadmap and prioritization.
- Data Modelling & Hypothesis Validation
- Develop and apply analytical and statistical models to validate innovation hypotheses and behavioural signals.
- Build reusable data frameworks and analytical modules to support experimentation and scalability.
- Design forecasting and predictive models to estimate market opportunity size and identify gaps that help in strategic decision making.
- Apply statistical methodologies and machine learning techniques, including regression, classification, clustering, and hypothesis testing, to strengthen analysis and enable data-driven decision making.
- Insight Communication
- Present insights in clear, structured, and decision-oriented formats for business stakeholders
- Translate analytical findings into actionable implications for product, design, and strategy teams.
- Develop intuitive dashboards and visualizations that focus on behavioral insights and decision relevance rather than vanity metrics.
- Proficient use of LLM model platforms such as Copilot Studio for research and discovery of insights.
- Bajaj Finance & Bureau Data Fluency
- Build a working understanding of BFL's customer, product, and transactional datasets across consumer durables, personal loans, EMI card, MSME, dealer finance, payments, and lifestyle/healthcare finance.
- Use bureau data from CIBIL, Experian, Equifax, and CRIF High Mark alongside internal data to enrich customer understanding — credit behaviour, risk profile, indebtedness, and life-stage signals.
- Blend internal behavioural data with bureau signals to build sharper customer cohorts and segments for research and design questions.
- Respect data governance, masking, and access norms applicable to credit bureau and customer data at all times.
Key Decisions / Dimensions
The Role Will Influence Or Make Decisions Related To
- Define behavioral metrics required to validate signals.
- Establish instrumentation frameworks for capturing customer interactions.
- Prioritize hypotheses for quantitative validation.
- Assess whether data supports scaling of innovation concepts.
- Enable evidence-led product and business decision-making
Major Challenges
- Bringing together the staggered datasets and data sitting in silos and connecting them to form a singular picture
- Identifying a building new data collection parameter which are not available today.
- Getting a background knowledge of business processes / product nuances by building data models
- Converting qualitative observations into measurable variables
- Differentiating noise from meaningful behavioral signals
- Ensuring data capture aligns with innovation goals (not just operational KPIs)
- Working with incomplete or early-stage datasets
- Balancing exploratory analysis with business constraints
- Bridging language gaps between research, design, product, and tech teams
Required Qualifications And Experience
- Qualifications
- Bachelor's or master's degree in: Data Analytics/Statistics/Computer Science/Engineering/Behavioral Science with strong quantitative exposure
- Work Experience
- Minimum 3-4 years of work experience in/similar role
- Analytical & Cognitive Skills
- Strong pattern recognition and systems thinking
- Ability to translate data into behavioral narratives.
- Hypothesis-driven analytical approach
- Comfort working with ambiguity and exploratory datasets.
- Quantification of qualitative insights
- Technical Skills
- Strong SQL proficiency (data extraction, joins, aggregations, cohort queries)
- Strong Databricks platform experience to develop Data Science and Analytics Models
- Proficiency in coding Languages like Python, SQL, R (Statistical Packages)
- Good presentation capabilities and report writing.
- Understanding of database structures and data warehousing
- Familiarity with experimentation frameworks (A/B testing)
- Ability to create structured data models.
