Sinke Legesse Lete

CVS Health — AI / ML Engineer
Mar 2023 – Nov 2025
• Built and deployed predictive models using XGBoost and CatBoost on large-scale patient datasets in Amazon Redshift, improving forecast accuracy by 18% and generating $1.2M in annual savings.
• Designed automated clinical scoring pipelines with AWS SageMaker, enabling scalable model training, validation, and deployment for healthcare applications.
• Developed production-grade NLP pipelines using BERT, LangChain, and Hugging Face to summarize patient documents, reducing manual review workload by 24% across 10,000+ weekly records.
• Engineered computer vision models (CNN, RNN, LSTM, YOLOv8) on medical imaging data in Amazon S3, improving anomaly detection and diagnostic efficiency by 21%.
• Implemented robust data and ML pipelines using Apache Airflow and AWS Glue, processing 200K+ records per day for feature engineering, clustering, and predictive analytics.
• Deployed low-latency inference APIs with FastAPI, optimized models via ONNX, and ensured explainability using SHAP and LIME, achieving 99% production uptime.

Northern Trust — AI / ML Software Engineer
May 2021 – Feb 2024
• Developed regression and classification models using Python, XGBoost, and CatBoost, improving portfolio risk prediction accuracy by 14% and preventing $2.3M in potential losses.
• Built NLP workflows with spaCy, BERT, GPT-4, and NER to automate regulatory document classification, reducing compliance review time by 27% across 1,200+ monthly reports.
• Implemented time series forecasting models on historical trading data using Pandas and NumPy, improving predictive accuracy by 17% with Azure ML and Databricks.
• Delivered audit-ready model explainability using SHAP, reducing manual reconciliation errors by 22% during portfolio compliance checks.
• Automated ML pipelines with Apache Airflow, Docker, Jenkins, and Azure Synapse Analytics, improving reproducibility by 95% and cutting deployment time by 35%.
• Designed Power BI dashboards backed by PostgreSQL and MongoDB, enabling real-time insights for 50+ portfolio managers handling 10,000+ daily transactions.

Booz Allen Hamilton — Data Engineer
May 2020 – May 2021
• Engineered secure and scalable data workflows in AWS supporting the National Cancer Institute (CTRP) project.
• Designed and maintained ETL pipelines using Python, improving data ingestion reliability and downstream analytics quality.
• Performed data enrichment, validation, and transformation to support analytics and ML use cases in healthcare and life sciences.
• Collaborated with data scientists and ML engineers to productionize ML-ready datasets for modeling and reporting.
• Supported ML-driven initiatives for federal clients including the CDC and FDA, ensuring compliance with data governance standards.

Nationwide Insurance — Data Science Intern
Aug 2019 – May 2020
• Delivered an end-to-end data science solution for Commercial Lines insurance, generating significant workload savings for the business unit.
• Built a scalable Microsoft Bing-based web scraper, performing extensive data cleaning, normalization, and validation.
• Implemented NLP semantic matching pipelines using TF-IDF and SIF weighted embeddings for data enrichment.
• Developed and tuned a bootstrap bias-corrected, cross-validated XGBoost model to automatically grade insurance risk.
• Applied business-driven thresholding strategies to align model outputs with underwriting and policy decision requirements.

Master’s Degree — Systems Engineering
2019 – 2020
• Golden Key International Honour Society, IEEE Student Branch
• Projects: Course Management System (Java/Swing, MySQL), Android Quality Management App for Military Use

Bachelor of Science — Electrical Engineering
University of Maryland | 2015 – 2018
• IEEE Student Branch Member
• Projects included Sign Language Recognition (Kinect), Obstacle Avoidance Robot, Signal Processing (ABR Filters), and Embedded Systems Design