Muhammad Ramzan

Senior Data Scientist

Senior data and platform engineer with a track record of shipping production AI systems, scaling real-time pipelines, and improving reliability and business outcomes in fast-paced environments. Over 11 years building cloud-native, API-driven products with CI/CD and Git-backed deployments, I combine Production ML and data product expertise with distributed-systems design using Kafka and Spark Streaming. I excel at cross-functional collaboration with Product and Engineering to drive measurable improvements in latency, availability, and operational cost. I bring hands-on experience in model serving, online inference, and observability tooling, and I apply technical agility and a commercial mindset to align engineering tradeoffs with product goals.

Experience

Jan 2019 — Present

Senior Data Scientist

ComScore

  • Owned end-to-end delivery of a core data product built on Python and Snowflake, designed external-facing REST endpoints for analytics consumers, introduced CI/CD and automated testing pipelines, and delivered 50+ logic enhancements that improved data delivery reliability for 10+ teams while reducing deployment rollbacks by 60%.
  • Packaged a Difference-in-Differences analysis into a production pipeline with automated scheduling and API outputs consumed across 20 countries, enabling product and commercial teams to make faster decisions and decreasing analytical turnaround time by 70% for regional campaigns.
  • Built and operationalized an XGBoost churn prediction model with model monitoring and alerting integrated into Grafana, which reduced month-over-month variance by 5% and supported a targeted retention program that improved resource allocation efficiency by 20%.
  • Maintained an Isolation Forest anomaly detection service, containerized for Kubernetes deployment and version-controlled with Git, reducing fraud-related incidents by 5% and lowering investigation workload through automated alerts and prioritized signal routing.

Apr 2021 — Jul 2022

Data Scientist (Contract)

Cloudeagle.AI

  • Designed and led a cloud-native product search platform on AWS, defining API surfaces and microservice boundaries to handle over 100,000 requests per minute while owning on-call reliability and CI/CD pipelines, resulting in 99.9% uptime and a 30% reduction in search error rates across production traffic.
  • Deployed vector-indexed TensorFlow models behind REST APIs and gRPC endpoints for semantic search, implemented model quantization and autoscaling inference clusters, reducing 95th percentile latency by 15% and increasing query throughput by 25% while maintaining A/B tested relevance improvements.
  • Architected real-time data pipelines using Kafka and Spark Streaming to deliver sub-second product search updates, implemented exactly-once processing semantics and retry strategies, which improved index freshness and reduced stale-search incidents by 40% for user-facing queries.
  • Collaborated with product and engineering teams to prioritize API features, established SLIs and Grafana dashboards, and instituted Git-based release gates, enabling weekly deployments and shortening incident-to-resolution time by 45% under ambiguous startup constraints.

Apr 2015 — May 2019

Statistical Analyst

ComScore

  • Established standardized primary and guardrail metrics in Grafana and Prometheus-based pipelines, automated alerting for data quality, and reduced recurring QA tasks by 20% while increasing stakeholder trust in reported KPIs for product and operations teams.
  • Developed a Naïve Bayes classifier in Python with scikit-learn and integrated it into an inference pipeline to enrich personalization signals, which improved campaign targeting accuracy and increased measurable sample sizes used in weighting for downstream analytics.
  • Optimized PySpark regression and strata analysis by vectorizing UDFs and tuning Spark configurations, reducing model runtime by two hours and improving nightly pipeline SLA compliance, which accelerated same-day reporting availability for analytics consumers.
  • Implemented scalable PySpark and SQL pipelines that unify billions of web-log records daily, introduced production deployment patterns and Git versioning, and improved data consistency to enable same-day reporting and faster troubleshooting across analytics teams.

Skills

  • React
  • TypeScript
  • Scala
  • gRPC
  • REST
  • Kafka
  • Spark
  • PySpark
  • TensorFlow
  • XGBoost
  • Docker
  • Kubernetes
  • GCP
  • AWS
  • Snowflake
  • SQL
  • NoSQL
  • Git

Education

Jan 2020

WorldQuant University

Master of Science in Financial Engineering · Financial Engineering

Jan 2010 — Dec 2014

Whittier College

Bachelor of Arts in Mathematics · Mathematics

Jan 2006 — Dec 2010

Emory University

Bachelor of Science in Mathematics & Economics · Mathematics & Economics