Just trying to get better at my craft &
Operations · Data · Strategy
I build revenue systems and data infrastructure that compound. 3 years across startups, VC, and global markets, I find the lever that moves the number.
Every number here has a story behind it. The case studies above tell those stories.
Built the upsell engine, the playbook, and the team motion from zero. 7.2x growth in 15 months.
Lead scoring model built on 4 segmentation variables (Firmographics, demographics, technographics, ICP analysis) sales team called the right people at the right time.
Python & SQL migration pipeline — 25% faster, 1.5% MoM churn held. Became the internal standard.
16 SME clients, project cycle time cut by 30% QoQ through process automation and clearer stakeholder comms.
Global VC fellowship, <1% acceptance. 7 investment committees across HealthTech, SaaS, FinTech, IoT, Mobility.
15+ cohorts taught. Sponsored by Morgan Stanley, Cognizant, Capgemini. Because the pipeline problem is solvable.
Not a list of logos. What I actually reach for and why.
The best way to understand how someone thinks is to read what they write. Replace these placeholders with your real pieces.
The full timeline. The stories are in the case studies above.
Not bullet points. The actual story — the problem, the thinking, the system, the result.
When I joined the upsell team, there was no system — just a list of customers and a vague mandate to sell more. The problem wasn't the product. It was that we had no idea which customers were ready to buy, when to reach them, or what to offer.
I started by segmenting the entire customer base using behavioural data — usage frequency, support ticket patterns, contract age. That segmentation became the foundation of a scoring model that ranked accounts by upsell readiness. We went from spraying and praying to calling the right customer at the right moment with the right offer.
Then I built the playbook: the sequence, the talking points per segment, the objection map. Trained 10 AMs on it. Launched cross-functional campaigns tying marketing touchpoints to sales outreach. Within 15 months, upsell ARR went from $53K to $3.47M.
Platform migrations are where customers quietly disappear. They hit friction, don't get support fast enough, and churn before you even notice. Our migration window was 8 months. 2,000+ customers. One bad data pipeline could have cost millions in ARR.
I built the migration pipeline in Python and SQL — but the real work was designing it to be fast enough to not create a backlog, and clean enough to not create data errors that would break customer workflows. Every edge case had to be handled before it became a support ticket.
We cut pipeline runtime by 25% and held churn to 1.5% MoM throughout — well below the industry average for migration periods. The system I built became the internal standard for all future migrations.
This could be your VC fellowship story — what did you actually learn from 60+ pitch decks? What would you have funded and why? Or it could be your Code First Girls story — how do you actually teach data science to 450+ women? What's the framework?
Replace this placeholder with your own story written in plain, honest prose. The more specific, the better.
Not values I put on a slide. Frameworks that show up in how I work every day.
15+ cohorts, 450+ women trained in data science. Sponsored by Morgan Stanley, Cognizant, Capgemini. Sept 2022–2024.
Remote technical training at Deloitte US — business-focused projects to drive workforce digitalization. March 2024.
TA for Business Stats, Data Manipulation & Visualization, and Financial Institutions & Markets.
Open to roles in Revenue Operations, Data Strategy, and Growth. Based in Karachi — open to global and remote work.