Business operations and strategic finance analytics
I am Syed Ahmed Basharat Ali, a data scientist and analytics engineer in New York City. At Geidea, Saudi Arabia's largest payments processor, I was promoted from Business Analyst to Data Scientist within 6 months and was a member of the executive strategy team.
Much of my work has been analysis that feeds an operating or financial decision: retention strategy, collections, reporting for leadership, and how much a policy or a hire is worth.
Strategy and finance analytics at Geidea (Aug 2023 to Jan 2025)
- Merchant churn prediction and revenue forecasting (ARIMA, logistic regression) informed a tiered retention strategy that cut merchant churn from 35% to about 10% on a $5M+ revenue portfolio.
- Automated collections pipelines contributed to $8M+ in additional annual profit.
- Built an automated Tableau platform for FinOps reconciliation, SLA tracking and interchange optimization across 5+ teams: 40% less manual reporting and 60% faster Oracle dashboards.
Leadership reporting at EY and Proxima AI
- EY (Jan 2022 to Jun 2023): founding member of the EY Pakistan Business Consulting data team. Analytics, predictive modeling, audit automation and C-suite dashboards for GSK and National Foods, with a 30% adoption increase across 5+ enterprise clients and a 50% team-efficiency gain. Built the internal reporting layer for partners and senior directors.
- Proxima AI (May 2021 to Jan 2022): data science solutions and reporting infrastructure contributing to 20% revenue growth.
Public work on operating decisions
In fraud-queue-economics, the fifth analyst returns $2,882 a month and the sixth returns $109, so the model answers how many analysts to hire. In ai-spend-intelligence, metered AI spend cannot be called before about day 12 of the month.
- Merchant freeze economics: 60,000 merchants; the 98%-recall freeze policy nets -$9.1M versus freezing nobody; cutting review time from 3 days to 1 is worth +$4.5M, more than a 2-point AUC gain (+$3.3M).
- Queue economics for a fraud and compliance review team: Alert recall 85.1% to 80.0%, dollar recall 76.8% to 93.0%, total cost -54%.
- AI spend intelligence from a raw card feed: 231 card descriptors resolved to 15 vendors; metered spend is not callable before about day 12 of the month, and is plus or minus 100% at day 5.
Tools
SQL, Python, R, ARIMA, Tableau, Power BI, Metabase, Oracle, Snowflake, dbt.
Contact
Email sabasharat.ali@gmail.com, or find me on LinkedIn and GitHub. Full history on the experience page.
Open to roles in New York or remote US.