Senior Product Data Analyst

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Senior Product Data Analyst

Fractal is a strategic AI partner to Fortune 500 companies with a vision to power every human decision in the enterprise. Fractal is building a world where individual choices, freedom, and diversity are the greatest assets. An ecosystem where human imagination is at the heart of every decision. Where no possibility is written off, only challenged to get better. We believe that a true Fractalite is the one who empowers imagination with intelligence. Fractal has been featured as a Great Place to Work by The Economic Times in partnership with the Great Place to Work® Institute and recognized as a ‘Cool Vendor’ and a ‘Vendor to Watch’ by Gartner.

Please visit Fractal | Intelligence for Imagination for more information about Fractal.

Position Overview:

Fractal is seeking an experienced and highly influential Senior Product Data Analyst. This is a client facing role where you will be a key strategic partner to product managers, engineers, and designers, using data to shape the future of our client’s products. You will be responsible for defining and analyzing metrics that measure success, conducting deep-dive analyses to uncover critical insights and opportunities, and driving a culture of data-informed decision-making. The ideal candidate is a master of their craft, possessing deep technical skills combined with strong business acumen and the ability to translate complex data into a compelling narrative that inspires action and drives product strategy. 

Roles and Responsibilities: 

Strategic Product Partnership: 

• Act as the primary data science partner for a core product area, influencing product strategy and roadmaps with data-driven insights. 

• Define and own the key performance indicators (KPIs) and measurement frameworks to evaluate product performance, user engagement, and the success of new features. 

• Lead proactive, in-depth analyses to identify and size new opportunities, understand ecosystems, and model the potential impact of product changes. 

• Translate ambiguous business questions into structured analytical problems, delivering actionable recommendations that drive tangible business outcomes. 

Advanced Analytics & Experimentation: 

• Design, execute, and analyze complex A/B and multivariate tests to optimize user experience, drive growth, and validate product hypotheses. 

• Develop sophisticated statistical models (e.g., predictive, clustering, causal inference) to understand user behavior, segmentation, and lifetime value. 

• Conduct deep-dive investigations into user journeys and product funnels to identify pain points and areas for improvement. 

• Build and maintain robust, automated dashboards and reporting systems to provide visibility into product health and performance for stakeholders at all levels. 

Technical Leadership & Mentorship: 

• Champion data best practices and raise the bar for data quality and analytical rigor across the organization. 

• Collaborate with Data Engineering to design and build scalable, high-quality data pipelines and data models that support analytical needs. 

• Mentor junior analysts and other team members, providing guidance on analytical techniques, experimental design, and technical skills. 

• Present findings and recommendations to senior leadership and broader cross-functional teams, effectively communicating the "so what" behind the data. 

Mandatory Technical Skills: 

• Expert-Level SQL: Demonstrated mastery of SQL for complex querying, data manipulation, and performance optimization across large, complex datasets. 

• Statistical Programming: High proficiency in a statistical programming language such as Python (Pandas, NumPy, SciPy, scikit-learn) or R. 

• Data Visualization & BI Tools: Extensive experience creating impactful visualizations and dashboards using tools like Tableau, Looker, Power BI, or similar. 

• Experimentation & Statistical Analysis: Deep understanding of experimental design (A/B testing, multivariate testing), statistical significance, and causal inference techniques. 

• Data Modeling: Experience with data modeling concepts and working with data warehouses (e.g., BigQuery, Redshift, Snowflake). 

Mandatory Non-Technical Skills: 

• Strong Product Sense & Business Acumen:<