Staff Machine Learning Engineer

Scott Heffley

Staff Machine Learning Engineer

****************@*******.*** 352-***-**** Gainesville, FL SUMMARY

Staff Machine Learning Engineer with 13 years of experience building scalable AI platforms, machine learning systems, and distributed data applications across healthcare technology, SaaS, analytics, and enterprise software environments. Strong background in Python, Machine Learning, Deep Learning, MLOps, cloud infrastructure, and backend engineering. Experienced delivering predictive analytics solutions, intelligent search applications, and production AI services supporting operational automation and enterprise decision-making. Proven ability to lead technical initiatives, improve platform reliability, mentor engineering teams, and deliver scalable production-ready systems in fast-paced engineering environments.

EDUCATION

Bachelor’s Degree in Computer Science

University of Florida Gainesville, FL 2009 - 2013 PROFESSIONAL SKILLS

• Programming Languages: Python, SQL

• AI/ML & Generative AI: Machine Learning, Deep Learning, Large Language Models (LLMs), Generative AI, Natural Language Processing (NLP), Predictive Modeling, Classification, Regression, Clustering, Anomaly Detection, Feature Engineering, Retrieval-Augmented Generation (RAG), Embedding Models

• ML Frameworks & Data Science: PyTorch, TensorFlow, scikit-learn, Pandas, NumPy, MLflow

• Cloud & MLOps: Docker, Kubernetes, CI/CD Pipelines, Azure, Azure OpenAI, Model Deployment, Model Monitoring

• Data Engineering & Distributed Processing: PySpark, Apache Spark, ETL Pipelines, PostgreSQL, MySQL, Distributed Data Processing, Data Ingestion

• Backend & Software Engineering: FastAPI, REST APIs, Backend Development, Microservices

• Soft Skills: Technical Leadership, Cross-Functional Communication, Mentoring, Problem Solving, System Design Collaboration, Analytical Thinking, Technical Planning, Stakeholder Communication WORK EXPERIENCE

Staff Machine Learning Engineer

Health Catalyst South Jordan, UT Jan 2025 – Present

• Led development of scalable AI/ML applications using Python, PyTorch, FastAPI, Docker, and Azure-based AI services supporting healthcare automation and enterprise operational workflows.

• Built retrieval-augmented generation (RAG) workflows using LLMs to improve internal knowledge discovery and reduce manual search effort for operational teams.

• Designed and maintained end-to-end machine learning pipelines covering ingestion, preprocessing, model training, deployment, monitoring, and automated validation processes.

• Developed backend inference services and REST APIs supporting low-latency AI applications used across internal healthcare operations.

• Integrated Generative AI capabilities into enterprise applications using Azure OpenAI and scalable inference workflows.

• Applied Deep Learning and anomaly detection techniques to healthcare operational datasets involving claims, provider, and encounter records.

• Improved deployment reliability and release consistency through standardized CI/CD workflows and automated validation processes across engineering environments.

• Enhanced enterprise search performance through scalable indexing workflows and optimized retrieval strategies supporting regulated healthcare systems.

• Collaborated with engineering, analytics, and product teams to translate operational requirements into scalable AI and software engineering solutions.

• Led architecture reviews, technical planning discussions, and mentoring initiatives supporting engineering quality and platform scalability improvements. Senior Data Scientist / Machine Learning Engineer

Forward Health San Francisco, CA Jul 2017 – Dec 2024

• Developed production machine learning and deep learning solutions using Python, TensorFlow, scikit-learn, SQL, and PySpark supporting healthcare analytics and operational automation initiatives.

• Built scalable feature engineering, preprocessing, and model evaluation workflows supporting predictive analytics systems processing millions of healthcare records annually.

• Designed and maintained ML pipelines for model training, deployment, validation, monitoring, and recurring retraining processes.

• Developed predictive models supporting operational forecasting, patient engagement analysis, workflow optimization, and anomaly detection initiatives.

• Implemented NLP-based text processing workflows supporting healthcare documentation analysis and operational reporting improvements.

• Built backend APIs and internal services integrating machine learning outputs into operational reporting and workflow automation platforms.

• Worked with distributed Spark processing workflows to support large-scale healthcare data preparation, transformation, and analytics tasks.

• Improved deployment efficiency and environment consistency through Docker-based workflows and automated CI/CD deployment processes.

• Led architecture discussions and implementation planning for scalable ML infrastructure supporting multiple healthcare analytics initiatives.

• Collaborated closely with engineering, analytics, and product stakeholders to deliver scalable AI solutions aligned with operational business goals. Data Scientist

CareCloud Miami, FL May 2014 – Jun 2017

• Developed analytical processing workflows, ETL pipelines, and operational reporting systems using Python, SQL, and enterprise reporting technologies.

• Built recurring transformation and validation pipelines supporting healthcare operational analytics and reporting initiatives.

• Applied statistical analysis, forecasting models, and exploratory data analysis techniques to identify business trends and improve reporting accuracy.

• Assisted with development of predictive analytics workflows supporting customer engagement analysis and operational performance tracking initiatives.

• Optimized reusable SQL queries, aggregation pipelines, and reporting workflows reducing manual processing effort across recurring reporting operations.

• Worked with PostgreSQL, MySQL, and cloud-based storage systems to improve reporting accessibility and operational data reliability.

• Collaborated with backend engineering teams to integrate analytical workflows and operational reporting services into internal platforms.

• Maintained monitoring scripts, validation routines, and automated processing workflows improving long-term reporting consistency and data quality.

• Participated in cloud migration initiatives and distributed processing improvements supporting analytics platform modernization efforts.

Data Analyst

YellowPepper Miami, FL Apr 2013 – Apr 2014

• Built operational dashboards, recurring reports, and SQL-based analytical workflows supporting payment operations and internal reporting initiatives.

• Developed data cleansing, transformation, and validation workflows improving reporting consistency and dataset accuracy.

• Assisted with ETL workflows, API-driven data extraction processes, and recurring reporting automation tasks.

• Worked with PostgreSQL, MySQL, and SQL Server environments to prepare datasets for operational analytics and reporting delivery.

• Applied statistical analysis and trend analysis techniques to identify operational inconsistencies and reporting gaps.

• Created reusable SQL scripts and automated reporting workflows reducing manual reporting effort for recurring operational requests.

• Supported engineering and analytics teams with data mapping, validation improvements, and recurring operational reporting requests.

• Participated in cross-functional discussions involving reporting requirements, data quality improvements, and operational analytics initiatives.

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