Mar 2024 – Jun 2025 · 1 yr 3 mos · New York, New York, United States · Hybrid

• 1. Led a new sales experience to accelerate payments deal processing by 22% for 4K+ global large-cap customers, by ideating and co-creating with 40+ users, defining a phased roadmap, piloting, launching, and training sales users.
• 2. Launched a new customer transfer experience, improving internal data assessment speed by 75% for 1K+ customers
• transferring from SMB to mid-market annually, unlocking a potential of $10MM+ annual revenue growth.
• 3. Enhanced global large-cap customer relationship reviews with 80% faster, automated summary views for 4K+ customers and 200K+ accounts to sales users, with user research, UI design, and payments data integration.
• 4. Expanded international adoption by 12% (EMEA & APAC market segments) for customers’ account summary views, by expanding global accounts & payments data, analyzing data, user testing, and GTM/training for sales adoption.

May 2023 – Aug 2023 · 3 mos · San Jose, California, United States · Hybrid

• 1. Increased sales enablement by 15% for the new Edge Processor product with SPL2, by identifying high-value use cases for Security & Observability, and developing 3 Go-To-Market (GTM) product demos.
• 2. Enhanced customer experience with 20% faster-to-build SPL2 pipelines on Edge processor, by interviewing 20+ customers, defining feature requirements, and building SPL2 functions.
• 3. Conducted competitive analysis for Edge Processor/SPL2 to define top 3 short-term and long-term differentiators.
• 4. Eliminated critical blocked searches issue impacting 20+ customers to save 60+ hours/month spent in mitigation, by analysing user behaviour in data ingestion, proposing solutions with pricing estimates, and executing.

Sep 2019 – Apr 2022 · 2 yrs 7 mos · Bengaluru, Karnataka, India

• 1. Enhanced customer satisfaction with 20x faster information updates and 10% reduced billing errors ($1M/month), by defining the business requirements and building a new customer-facing billing portal for digital advertising customers.
• 2. Increased monthly revenue by ~€14M (10% increase) with a new digital advertising fee in European countries, by driving AWS cloud technical design, and cross-functional execution across data warehousing, billing, and tax teams.
• 3. Streamlined new ad program onboarding by 2x (1 month to 2 weeks) for Performance Ads, by defining product requirements, designing technical architecture, and developing a scalable service on AWS cloud infrastructure.
• 4. Expanded Display Ads automated billing to a new market segment (India) for 500+ advertisers with revenue of 10M+ INR/month, by collaborating cross-functionally across business, tax, and engineering teams.
• 5. Reduced billing errors by ~20% and saved 10+ hours/month, with a 1-click Display Ads billing automation and SOPs.

Jul 2019 – Sep 2019 · 2 mos · Bengaluru, Karnataka, India · On-site

• An early-stage startup which builds AI cloud data pipelines to efficiently fetch data from edge-to-core for self-driving cars.
• 1. Created an internal analytics product to analyze data based on time and features for 4 standard self-driving car datasets.
• 2. Built edge-to-core AI data pipelines to sample ~30% of the most useful data, and filter ~10% noise from the self-driving car data.

May 2018 – Jul 2019 · 1 yr 2 mos · Bengaluru, Karnataka, India · Hybrid

• An early-stage FinTech startup which builds a simplified goal-based platform for financial planning in India.
• Built the zero-to-one mobile app with UI/UX features for financial goal planning, by working closely with the founders to collaborate on product vision, roadmap and strategy. This now has 80k+ users and 4M+ INR AUM.

Jun 2017 – Jul 2017 · 1 mo · Greater Kolkata Area

• Collaborated on an ongoing research project for preventing cancerous cells. Applied a Machine Learning multi variable (20+) regression model with ~95% accuracy to simulate cancer factors; and designed a Heuristic optimization controller for prevention.
• Models : Single Neural Network, Multi neural network, Convolutional Neural Network, Linear Regression, Decision Trees, etc.