Professional Resume Template for
Data Science Engineer
Jonathan K. Sterling
San Jose, CA
(408) 555-0143
jonathan.sterling@email.com
linkedin.com/in/jonathan-sterling | github.com/jsterling-ds | jsterling-ds.dev
Professional Summary
Systems-oriented Data Science Engineer with 5 years of experience designing, training, and productionizing machine learning pipelines within high-scale enterprise environments. Expertise includes optimizing distributed data pipelines, implementing automated MLOps workflows, and building real-time prediction services. Successfully reduced model deployment latency by 35% and increased training efficiency by 28% through custom distributed processing frameworks. Proficient with Python, PyTorch, Apache Spark, Docker, Kubernetes, and AWS SageMaker.
Work Experience
Data Science Engineer — Novasphere Platforms
San Jose, CA | July 2023 – Present
- Engineered a continuous model training and deployment pipeline using AWS SageMaker and GitLab CI/CD, reducing the time-to-production for recommendation models from 14 days to under 4 hours.
- Optimized distributed feature extraction pipelines in Apache Spark, processing over 12TB of active user telemetry daily and reducing cloud compute costs by 22% ($45,000 annually).
- Redesigned the deep learning model architecture for search ranking, yielding a 14% improvement in click-through rate (CTR) and a 9% reduction in model inference response time.
- Deployed real-time model drift monitoring systems across 8 production endpoints, flagging performance drops and reducing prediction error rate by 18% over a 6-month operation period.
Associate Data Science Engineer — Stellaris Data Systems
San Francisco, CA | August 2021 – June 2023
- Constructed custom offline feature stores utilizing Redis and PostgreSQL, supporting 4 parallel machine learning products and cutting duplicate feature queries by 32%.
- Developed and optimized 12 REST API endpoints using FastAPI and Docker to serve model predictions, maintaining sub-45ms latency under a peak load of 5,500 requests per second.
- Designed and executed automated A/B test suites for user segmentation algorithms, validating model improvements across 1.2M active accounts and improving conversion rates by 8%.
- Refactored training data preprocessing pipelines using Dask and Pandas, accelerating training run preparation times by 41% and saving 15 engineering hours weekly.
Education
Bachelor of Science in Data Science
Santa Clara University · Santa Clara, CA · 2021
Skills
Python, R, SQL, PyTorch, TensorFlow, Keras, Scikit-learn, Hugging Face, NumPy, Pandas, Dask, Apache Spark, Apache Kafka, Docker, Kubernetes, AWS SageMaker, MLflow, Git, GitLab CI/CD, Redis, PostgreSQL, Snowflake
Projects
Enterprise Customer Churn Predictor
Role: Lead Data Science Engineer
Tools: Python, PyTorch, Docker, Kubernetes, AWS
Built and deployed an end-to-end churn prediction service, processing 5M daily user profiles, which reduced customer attrition by 11% and saved $180,000 in monthly revenue.
Real-time Fraud Detection Engine
Role: Data Science Engineer
Tools: Python, Apache Kafka, Apache Spark, MLflow
Architected streaming ingestion pipelines for real-time anomaly detection, reducing the false positive rate by 24% while scanning 2,000 transactions per second.
Certifications
- Google Cloud Professional Machine Learning Engineer (2024)
- AWS Certified Machine Learning - Specialty (2022)
- Microsoft Certified: Azure AI Engineer Associate (2023)
Additional information
- Languages: English (Native), Spanish (Professional Working)
- Publications: Co-author of 'Scalable Machine Learning Workflows in Cloud Environments' (2023)
- Availability: 3 weeks notice
Job Market Insights
Market data and opportunities for
Data Science Engineer
Job Market Insights
$115,000
-
$175,000
Avg:
$142,000
Growth Outlook:
The demand for Data Science Engineers in the United States continues to accelerate, with employment projected to expand by 34% from 2024 to 2034, according to the Bureau of Labor Statistics. This growth is propelled by the integration of artificial intelligence and machine learning technologies across diverse sectors including finance, healthcare, and e-commerce. As companies shift toward production-level AI, engineers who can bridge the gap between software systems and machine learning models are highly sought after. Annual job openings are expected to average over 23,400 during this period.
34% growth over 10 years
Key Skills Required
Focus on these skills when customizing your resume for recruiter screenings.
Search Jobs
Explore live openings for
Data Science Engineer
roles and tailor your resume to match the market demand.
Search
Data Science Engineer
FAQ
Common questions about the
Data Science Engineer
position