Lead analytics projects within the Health Plan Analytics team. Implement creative automated solutions, including machine learning models to enhance the impact of projects. Advise and collaborate with senior leaders on data collection techniques and interpretation of project findings.

Developed and implemented creative automated business and reporting solutions within the Health Plan Analytics team. Worked with stakeholders to ensure Power BI reports provided actionable insights while also conducting data science projects to further enhance business operations.

Combining my experience as a physiologist, data scientist, and coach to develop a tool that helps individuals effortlessly develop nutritious meal plans to achieve their health, wellness, and performance goals.

Coach athletes of varying disciplines, ability levels, and backgrounds while incorporating the latest research on exercise performance. Develop personalized training plans based primarily on heart rate and cycling power output and also provide guidance on nutrition, recovery, and race-day tactical strategies.

Responsible for designing and executing statistical analyses, machine learning models, data mining, predictive modeling, and visualizations that drove patient and business insights. Also developed scientific communications and assisted in the development of DoD research proposals to secure funding.

Studied the role of physical activity and fitness for improving health. Developed study proposals, obtained/maintained research study ethics approval, collected data, managed well-established databases, performed statistical analyses, and summarized findings for publications and presentations. Projects involved international collaborations and utilized R and Python:• Predicting Mortality Risk from Cardiopulmonary Exercise Test Variables: Built Cox proportional hazard models to examine the prognostic potential of different variables measured during an exercise test to improve patient risk stratification and clinical care.• Predicting Cardiorespiratory Fitness: Created machine learning prediction models and statistically assessed their utility in clinical and epidemiological settings to address the American Heart Association recommendation to predict fitness (VO2max) when it is not feasible to measure.• FRIEND Web App: Created an automated data pipeline that visually summarizes data within FRIEND and develops a machine learning model to predict a user’s fitness level and percentile. A list of FRIEND publications is also collected via webscraping. The web app (https://share.streamlit.io/jimpeterman/friend_app/main/app.py) is easily updated as data is added to the FRIEND database. • Created Reference Standards for Exercise Test Variables: A number of variables measured during a cardiopulmonary exercise test can be used to assess patient health. These projects created reference standards to provide clinicians and exercise physiologists the ability to better interpret patient results and thus improve patient care. • Examined Associations Between Commuting and Cardiovascular Disease: Created linear and non-linear regression models to analyze data from the CDC and US Census. Findings indicated walking and cycling modes of commuting are associated with lower risk for cardiovascular disease and strengthened the case for promoting active commuting to improve health.

Taught Fitness Assessment in Exercise Science (EXSC 202), a laboratory-based course designed to introduce rationales and procedures of various health and fitness assessments including blood pressure, body composition, aerobic capacity, and muscular strength.

Completed accredited data science program focused on data engineering, machine learning, and communication of deliverables.

Analyzed the effects of a multi-component workplace sitting intervention. Using linear mixed models and standardized effect sizes, found measures of productivity and stress improved, providing further support for interventions that reduce sitting and enhance health in the workplace. Also assisted with the implementation of a novel online toolkit ("BeUpstanding") to reduce sitting and promote physical activity in the workplace.

Taught Human Physiology 2 (IPHY 3480), the second semester of a two-semester upper division sequence required for Integrative Physiology majors. Covered the physiology of the digestive, metabolic, cardiovascular, respiratory, urinary, and reproductive systems.

Collaborated with industry partners, government agencies, and other researchers to study novel physical activity interventions:• Assessing E-Bike Commuting: Implemented an e-bike commuting intervention that found improvements in health (fitness and blood glucose regulation) and that the bikes were used at safe riding speeds. Of note, the City of Boulder used these result to justify the use of e-bikes on existing cycle paths. • Benefits of a Cycling Workstation: Implemented a 4-week intervention in real-world workplaces in which participants used a small cycling ergometer at their desk throughout the day. Found significant improvements to markers of health (fitness and cholesterol levels). • Passive Cycling to Improve Health: Examined passive cycling in a laboratory setting. Found increases in energy expenditure, improvements in blood glucose regulation, and no impact on cognition, suggesting passive cycling could be used to improve health.In addition to the primary work of my dissertation and with a strong interest in athletic performance (competed as an elite cyclist throughout school), I also compared various ways to predict cycling performance and found aerodynamics was the best predictor when cycling over level terrain. I also examined the accuracy of predicting energy expenditure during various modes of exercise.

Responsible for teaching laboratory classes covering the scientific method as well as the cardiovascular, respiratory, reproductive, and endocrine systems.