Tejas Pandey

Collaborated cross-functionally with product, design, and engineering teams to enhance user experience across 4 core features, defining product requirements and supporting feature prioritization, contributing to a 30% increase in user engagement. Analyzed user behavior data and qualitative feedback to identify onboarding and feature-adoption friction points, contributing to product changes that improved adoption and retention by 15%. Tracked and analyzed post-launch performance using 25 engagement metrics and KPIs, enabling data-informed iteration.

Associate Product Manager - Supersquad - Gurugram, India

(2025-03)

• Collaborated cross-functionally with product, design, and engineering teams to enhance user experience across 4 core features, defining product requirements and supporting feature prioritization, contributing to a 30% increase in user engagement.
• Analyzed user behavior data and qualitative feedback to identify onboarding and feature-adoption friction points, contributing to product changes that improved adoption and retention by 15%.
• Tracked and analyzed post-launch performance using 25 engagement metrics and KPIs, enabling data-informed iteration.

Product Analyst - Sportskeeda - Gurugram, India

(2024-08 - 2025-02)

• Developed and optimised BigQuery and SQL-based dashboards covering 35 user engagement metrics, enabling sharper data-driven content strategies and significantly improving overall user retention by 15%.
• Led a cross-functional initiative with design, product, and tech teams to streamline data flow and optimise reporting pipelines, significantly improving team-wide reporting efficiency and reducing analysis time by 25%.
• Automated recurring data extraction and reporting workflows using Python across 12 datasets, boosting overall data accuracy and reducing manual workload by 30%, thereby enabling faster, high-quality insights across teams.

Risk Analyst Intern - Simpl - Bengaluru, India

(2024-01 - 2024-07)

• Cleaned, structured, and validated 30K transaction records, significantly strengthening core data quality and enabling deeper, more reliable analyses that directly supported strategic leadership decision-making.
• Implemented fraud detection logic across user cohorts, uncovering high-risk behavioural patterns and contributing to a measurable 30% reduction in fraudulent transactions across the organisation's key product lines.
• Applied machine learning techniques to analyse 15 data points, improving fraud detection accuracy by 40% and reducing false positives, resulting in far more efficient and focused risk operations.

Bachelor of Engineering - Electronics and Communication - Thapar University of Engineering and Technology (2020-09 - 2024-07)