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David Oliver

@davidoliver

Data Science Engineer at EchoCommerce who serves 20M daily personalized recommendations with median latency under 100ms.

United States

Message

What I'm looking for

I'm looking for a mid-level Data Science Engineer role in the Bay Area, on-site or hybrid. I'm willing to relocate and travel as needed.

At EchoCommerce, I designed and implemented a real-time personalization pipeline serving 20M recommendations a day with median latency under 100ms. I also deployed CTR and conversion models that increased recommendation CTR by 12% and incremental revenue per user by 6%.

At FinSight Analytics, I built a fraud detection pipeline that achieved 92% recall at 5% FPR and reduced fraud losses by about 18% year over year. Earlier, at BrightMetrics, I analyzed product experiments and built churn models; my onboarding experiment analysis contributed to a 9% increase in 7-day retention.

Experience

Work history, roles, and key accomplishments

Current

Data Science Engineer

Current

EchoCommerce

Jun 2023 - Present (3 years 5 months)

Led design and implementation of a real-time personalization pipeline using Kafka, Spark Structured Streaming, and Redis, serving 20M personalized recommendations per day. Developed and deployed CTR and conversion models that increased recommendation CTR by 12% and incremental revenue per user by 6%.

Kafka Spark Structured Streaming Redis LightGBM MLFlow Prometheus Grafana Model Monitoring Data Drift Detection Mentorship On Call

Machine Learning Engineer

FinSight Analytics

Sep 2020 - May 2023 (2 years 8 months)

Built a fraud detection pipeline ingesting 1.2M events per day using Spark and feature engineering, achieving 92% recall at 5% FPR and reducing fraud losses by 18% year-over-year. Orchestrated daily training pipelines with Airflow and Databricks, reducing retraining latency from 6 hours to 90 minutes.

Spark Feature Engineering Airflow Databricks Docker Kubernetes REST APIs Parquet ETL Optimization

Data Analyst / Junior Data Scientist

BrightMetrics

Jul 2018 - Aug 2020 (2 years 1 month)

Performed exploratory analysis and A/B testing for product teams, leading an onboarding flow experiment that increased 7-day retention by 9%. Developed automated reporting dashboards in Looker and Tableau, decreasing ad hoc report requests by 40%.

Exploratory Data Analysis A B Testing Looker Tableau Logistic Regression Tree Models Churn Analysis