Jashyant Sikhakolli

About Jashyant Sikhakolli

Staff Data Scientist | Driving $7M+ in AI-Powered Product Growth I specialize in transforming complex AI capabilities into measurable business outcomes. As a Staff Data Scientist at Intuit, I led the product analytics strategy for the AI Customer Agent launch, scaling the platform to 55K+ active users in 8 weeks and quantifying a multimillion-dollar revenue impact.Core Impact & Achievements: Monetization Strategy: Identified a $7M -$8M incremental annual revenue opportunity by linking AI agent adoption to subscription upgrades and expansion modeling.Causal Rigor: Isolated the true impact of AI features using Difference-in-Differences (Diff-in-Diff) and Synthetic Controls, proving a 15% lift in adoption despite seasonal \"noise.\"Stakeholder Enablement: Architected custom GPTs to automate trend analysis for PMs and Marketing, reducing manual ad-hoc requests by 50% and accelerating time-to-insight by 60%.Scalable Infrastructure: Re-engineered ETL pipelines using PySpark and AWS, slashing data latency from 24 hours to 2 hours for real-time performance monitoring.Predictive Excellence: Developed an XGBoost-based Customer Health Score at Oracle (20% lift in retention) and saved $2.4M in operational costs at Optum via predictive modeling.The Staff-Level Edge: I bridge the gap between technical engineering and executive strategy. Beyond the data, I am committed to mentoring junior analysts and championing a high-performance culture built on empathy and transparency. I am seasoned in presenting data-driven trade-offs to VP/SVP stakeholders, mediating conflicting roadmaps to ensure product success is built on a foundation of data integrity.Technical Mastery: Experimentation: A/B Testing, Causal Inference (Propensity Score Matching, Synthetic Controls, Diff-in-Diff).Product Analytics: LTV Modeling, Churn/Retention Analysis, Cohort Analysis, North-Star Metric Design.Machine Learning & AI: XGBoost, Predictive Modeling, LLM Performance Monitoring, Custom GPTs.Stack: SQL, Python, PySpark, AWS (S3, Redshift), Tableau, PowerBI.Seeking: I am an active candidate for Staff or Senior level roles in Product Data Science or Data Analytics space. I specialize in causal inference, LLM evaluation, and executive-level product growth strategy. Open to relocation. Feel free to reach out via DM or at j•••••••@gmail.com to discuss how I can help your team scale.#DataScience #ProductAnalytics #AI #GenerativeAI #ProductGrowth #StaffDataScientist #MachineLearning #LLM #CausalInference #SaaSGrowth

Experience

1. Staff Data Scientist - Product

   Intuit

   Apr 2024 — Present · Atlanta, GA, US

   Product Growth: Designed the analytics framework for the Customer Hub launch, defining ‘Activation’ metrics to track Feature Adoption; achieved a 15% lift in agent interactions through optimized onboarding flows• Experimentation: Executed A/B tests & causal studies (Difference-in-Differences, Propensity score matching, Synthetic Controls) to isolate the AI Agent impact on adoption (+10%) & retention, successfully controlled for baseline variances• User Funnel Analysis: Instrumented event tracking across all features in Customer Hub to identify key drop-off points in the AI Agent journey, implemented data-driven feature improvements that drove a 25% increase in active usage and significantly boosted long-term User Retention• Monetization Estimation: Quantified the impact of Customer Hub adoption on LTV (Lifetime Value) by linking agent interactions to subscription upgrades, projecting $7-8M in incremental annual revenue• Monetization Strategy: Developed a propensity-to-upgrade model to identify high-value SMB power users, resulting in a targeted GTM strategy that converted 12% of Beta users and generated $4M in annual revenue (contributing to the $7-8M)• Infrastructure & Latency: Re-engineered ETL pipelines using PySpark to reduce data latency from 24 hours to 2 hours, enabling real-time product monitoring and reducing ad hoc executive data requests by 50%• Strategic Stakeholder Alignment: Led Data Science team in a cross functional environment (Product, Engineering, Design, Marketing) to define ‘Activation’ and ‘Success’ criteria for the AI Customer Agent launch; mediated conflicting team roadmaps by presenting weekly data-driven trade-off analyses to VP/SVP leadership for a seamless Beta-to-GA transition for 6M users

Education

• Birla Institute of Technology and Science, Pilani

  Bachelor's degree, Pharmaceutical Sciences

  2012 — 2016

• UMN Carlson School of Management

  Master of Science - MS, Business Analytics

  2019 — 2020