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.
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
- Behavioral & Journey Analytics (Governance-Led)
- Define clickstream and journey measurement strategy (what to track, where, and why).
- Establish event taxonomy, tracking standards, and governance frameworks across platforms.
- Partner with engineering and analytics teams for implementation and data pipeline alignment.
- Analyze large-scale behavioral datasets using SQL, Python, and PySpark.
- Leverage tools such as GA4, Adobe Analytics, Clevertap, and Power BI to generate actionable insights.
- Ensure data consistency, interpretability, and business relevance of behavioral metrics.
- Perception & Insighting
- Measure and analyze customer perception across journeys, features, and experiences.
- Integrate Voice of Customer metrics (NPS, CSAT, CES, feedback loops) with behavioral data.
- Conduct structured insighting to identify patterns in engagement, satisfaction, churn, and retention.
- Validate hypotheses emerging from generative research and customer Signals.
- Build causeeffect narratives linking experience design to business and customer outcomes.
- Translate complex datasets into clear, actionable insights influencing roadmap and prioritization.
- Data Modeling & Hypothesis Validation
- Design analytical models to validate innovation hypotheses.
- Create reusable data modules to support experimentation and signal quantification.
- Data Extraction & Engineering Collaboration
- Fetch and manipulate data from internal databases
- Build queries and structured datasets for analysis
- Develop reusable data modules for innovation experiments
- Work closely with data engineering teams for scalable pipelines
- Insight Communication
- Present insights in clear, decision-oriented formats
- Translate analytics into implications for product and design teams
- Create structured dashboards focused on behavior, not vanity metrics
Key Decisions / Dimensions
The Role Will Influence Or Make Decisions Related To
- What behavioral metrics are required to validate a Signal
- How customer interactions should be instrumented
- Which hypotheses require quantitative validation
- Whether data supports scaling an innovation concept
- Evidence led product/business decisions
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)
- Working knowledge of Python / R for data analysis
- Experience with data visualization tools (Power BI, Tableau, etc.)
- Understanding of database structures and data warehousing
- Familiarity with experimentation frameworks (A/B testing)
- Ability to create structured data models
