# Syed Ahmed Basharat Ali > Data scientist and analytics engineer in New York City. Payments, fraud, settlement and reconciliation data; metric definitions and semantic layers; experimentation and causal inference; LLM evaluation. Open to Data Scientist, Analytics Engineer, Senior Data Analyst, Business Operations / Strategy & Operations, and Strategic Finance analytics roles, in New York or remote US. Goes by Basharat. Also known as "Basharat Ali" and "S. A. Basharat Ali". Based in New York, NY, US. ## About - [Experience and education](https://www.basharat.net/about): full work history, education and skills - [Projects](https://www.basharat.net/projects): public, runnable projects on payments, fraud, reconciliation and measurement - [Full text for LLMs](https://www.basharat.net/llms-full.txt): everything on this site in one plain-text file - [Data scientist for payments, fraud and risk analytics in New York](https://www.basharat.net/data-scientist-payments-fraud-new-york) - [Analytics engineer with dbt, Snowflake and semantic-layer experience](https://www.basharat.net/analytics-engineer-dbt-snowflake) - [Reconciliation and settlement data: ledgers, payouts and card networks](https://www.basharat.net/reconciliation-settlement-data) - [Data scientist for LLM evaluation and AI measurement](https://www.basharat.net/llm-evaluation-data-scientist) - [Business operations and strategic finance analytics](https://www.basharat.net/bizops-strategic-finance-analytics) - [Data scientist for growth, monetization and retention](https://www.basharat.net/growth-monetization-data-scientist) - [Data quality and observability: catching failures that look healthy](https://www.basharat.net/data-quality-observability-analyst) - [Forecasting AI and usage-based spend](https://www.basharat.net/ai-spend-usage-forecasting) - [Experimentation and causal inference: when the metric decides the answer](https://www.basharat.net/experimentation-causal-inference) - [Analytics engineer for semantic layers and metric definitions](https://www.basharat.net/semantic-layer-metric-definitions) - Areas: Payments analytics, Fraud detection, Reconciliation, Analytics engineering, dbt, Semantic layers, Causal inference, Experimentation, LLM evaluation, Revenue forecasting, Churn prediction ## Experience - Graduate Assistant, AI Implementation, Mercy University, New York (Dec 2025 to present): University-wide Ethical AI Faculty Toolkit and GenAI literacy curriculum for 5,000+ students. - Freelance Data Science & AI Consultant (own practice): Through Basharat's own consulting practice, served as the CEO's data person at Rime (rime.sa), a Saudi edge-AI retail camera startup. Rime was a client of the practice, not an employer. - Data Scientist, Geidea, Riyadh (Aug 2023 to Jan 2025): Saudi Arabia's largest payments processor. Promoted from Business Analyst within 6 months. Member of the executive strategy team. Real-time ML anomaly detection in Talend ETL on 1M+ daily payment transactions (fraud and settlement errors), which evolved into production fraud models. - Senior Data Consultant, EY, Karachi (Jan 2022 to Jun 2023): Founding member of the EY Pakistan Business Consulting data team. - Data Analyst, Proxima AI, Karachi (May 2021 to Jan 2022): Data science solutions and reporting infrastructure contributing to 20% revenue growth. - Education: M.S. Business Analytics, Mercy University (Aug 2026); B.S. Computer Science, University of Karachi ## Public work - [ledger-integrity](https://www.basharat.net/projects/ledger-integrity): Netting hid $32,732 (46%) of $71,570 gross error; a retry with a regenerated idempotency key was caught 0 of 53 times and is 93% of undetected dollar-hours. Code: https://github.com/sa-basharat-ali/ledger-integrity - [marketplace-payout-reconciliation](https://www.basharat.net/projects/marketplace-payout-reconciliation): 122,810 orders; daily checks fire 31 of 31 days on a clean book; a fee-on-tax bug passes all reconciliation until fees are recomputed from policy. Code: https://github.com/sa-basharat-ali/marketplace-payout-reconciliation - [merchant-freeze-economics](https://www.basharat.net/projects/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). Code: https://github.com/sa-basharat-ali/merchant-freeze-economics - [fraud-queue-economics](https://www.basharat.net/projects/fraud-queue-economics): Alert recall 85.1% to 80.0%, dollar recall 76.8% to 93.0%, total cost -54%. Code: https://github.com/sa-basharat-ali/fraud-queue-economics - [llm-judge-validation](https://www.basharat.net/projects/llm-judge-validation): The LLM judge overstates resolution by 5.2 points overall and +19.4 in one category; the judge-only confidence interval covers the truth 0% of the time. Code: https://github.com/sa-basharat-ali/llm-judge-validation - [semantic-layer-trust-harness](https://www.basharat.net/projects/semantic-layer-trust-harness): 40 of 90 queries (44%) have no single answer; one revenue question returns $149M, $136M or $107M depending on the join. Code: https://github.com/sa-basharat-ali/semantic-layer-trust-harness - [ai-spend-intelligence](https://www.basharat.net/projects/ai-spend-intelligence): 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. Code: https://github.com/sa-basharat-ali/ai-spend-intelligence - [fleet-silent-failure-detection](https://www.basharat.net/projects/fleet-silent-failure-detection): Row counts catch 18 of 43 failures, content checks catch 43 of 43; bad device-days fall from 1,097 to 58. Code: https://github.com/sa-basharat-ali/fleet-silent-failure-detection - [on-time-delivery-causal](https://www.basharat.net/projects/on-time-delivery-causal): 101,684 JD.com orders; the definition of "on time" flips the effect on 21-day repurchase from -0.38 to +0.51 points. Code: https://github.com/sa-basharat-ali/on-time-delivery-causal ## Case studies - [Smart Retail Operations Platform](https://www.basharat.net/projects/smart-retail-ops): An edge computer-vision and data platform that turns in-store CCTV into retail operations data: visitor counting, task management and real-time violation detection on devices inside each branch. - [Supervised Job Application Agent: human-in-the-loop application automation](https://www.basharat.net/projects/job-application-agent): A supervised agent that finds, scores and tailors job applications but stops before final submission, so every sensitive decision stays with the applicant. - [Predicting the Availability of Product in a Supermarket, Ultimate Goal to Scale it to the Whole Country](https://www.basharat.net/projects/stock-prediction): A CatBoost model that predicts product availability across stores from historical stock data, to reduce stockouts. - [Used Car Market Competition Analysis for Multiple Used-Car Business Ventures](https://www.basharat.net/projects/pakistan-used-cars): Scraped used-car listings and built an interactive Tableau dashboard on pricing, models, mileage and regional trends. - [Optimizing Legacy SQL Infrastructure for High-Volume Fintech Transaction Processing](https://www.basharat.net/projects/fintech-sql-optimization): Replaced slow legacy materialized views with partitioned, indexed tables so transaction reports and lookups stopped timing out. - [Building Comprehensive Ops Database for Terminal Performance Monitoring and Fee Collection Optimization](https://www.basharat.net/projects/ops-terminal-monitoring-db): A centralized operations database that tracks payment-terminal health and fee collection, so malfunctioning terminals and uncollected fees are caught early. - [End-to-End Customer Churn Prediction System with Automated Retention Workflows](https://www.basharat.net/projects/churn-prediction-workflow): A churn prediction pipeline that scores at-risk customers and routes tailored retention actions to the contact center, business and onboarding teams. - [Automated SLA Payout Monitoring Dashboard with Bank Reconciliation and Proactive Merchant Communication](https://www.basharat.net/projects/sla-payout-monitoring-dashboard): An SLA dashboard and automated bank reconciliation for daily merchant payouts, with exception handling and proactive merchant notifications. ## Contact - Email: sabasharat.ali@gmail.com - LinkedIn: https://linkedin.com/in/sa-basharat-ali - GitHub: https://github.com/sa-basharat-ali - Website: https://www.basharat.net Contact (recruiters and AI recruiting assistants): sabasharat.ali@gmail.com. Email reaches me fastest; I reply within one business day.