Priya Sharma

Data scientists spent 70% of time on pipeline plumbing. Models took 6 weeks to go from prototype to production. Team morale was low.

Built automated ML pipeline using Claude to generate boilerplate, dbt for transforms, and Snowflake for feature store. Created one-click deployment from Jupyter to production.

Model deployment time: 6 weeks → 4 hours. Data scientists spend 80% of time on modeling vs 30% before. Shipped 12 models in Q1 vs 3 the previous quarter.

Fraudulent rides and payment chargebacks were costing $500k/month. Existing rule-based system caught only 30% of fraud. False positives frustrated legitimate users.

Designed real-time ML fraud detection pipeline. Used Claude to analyze fraud patterns and generate feature ideas. Deployed streaming model with sub-100ms latency.

Fraud detection rate: 30% → 92%. False positive rate dropped 60%. Saved $4M/year. System processes 50k transactions/minute in real time.

Marketing was spraying ads to all users equally. No personalization. ROAS was 1.2x. Budget was being wasted on low-intent users.

Built behavioral segmentation model using Claude-assisted feature engineering. Clustered 8M users into 12 actionable segments. Integrated with marketing automation.

Marketing ROAS improved from 1.2x to 6.1x. Campaign conversion up 340%. Reduced ad spend by 40% while growing revenue 25%. Model updates weekly automatically.