Experience

Data Scientist II, NetApp, December 2021 – Present
NetApp ($6B in annual revenue) engineers, manufactures, and sells enterprise data storage devices, professional services, cloud offerings, and storage cost-optimization software.
• Built a forecasting framework that cut error 30-50% relative to the sales forecasts in our core business and cut process run-time from days to hours. This was achieved using a global XGBoost model, parallelization on a google cloud instance, and ensemble methods.
• Championed the above results and expanded forecasting services to cover all aspects of the business, including parts supply planning (units), cloud operations (ARR, ACV, iARR), and partner and channel compensation planning ($ booked).
• Assembled and lead a team in a company-wide Generative AI Hackathon. Our team made the round of finalists and took 5th place of 36 teams. We built an app that accelerates sales engineer tasks by vector embedding the NetApp knowledge base to give GPT4 more context.
• Deployed a deal pricing app using the R shiny framework hosted securely within NetApp intranet using Docker.
• Led requirement setting and documentation with IT during a database and CRM provider transition, ensuring continuity of forecast delivery. Cross-trained new teammates on forecast production and monitoring during a re-structure.

Data Science Intern, NetApp, May 2020 – December 2021
• Constructed a data pipeline that scraped economic data from FRED and Google trends to use in forecasts. This improved forecast performance by 10%. Owned forecast refresh meetings, answering stakeholder questions.
• Led a margin vs revenue trade-off analysis that guided sales strategy and supported pricing and propensity projects.

Data Science Intern, Lawrence Livermore National Lab, June 2021 – August 2021
I worked in the Functional Materials Synthesis and Integration group and with my larger cohort in the Data Science Summer Institute to apply machine learning to problems ranging from chemical property prediction to asteroid detection to reinforcement learning agents.
• Applied multiple ML models, including graph neural networks, to predict chemical properties in support of the labs directive to protect and advance US interests.
• Built a computer-vision model for asteroid detection using CNN architecture.

Ph.D. Research Student, South Dakota State University, August 2017 – December 2021
My work in the “Green Microbiology” lab made strides towards harnessing biological nitrogen-fixation for the development of self-fertilizing crops. Self-fertilizing crops will feed growing populations and decrease hazards from synthetic fertilizer run-off.
• Designed controlled experiments and computational approaches to elucidate the molecular drivers of oxygen-tolerant nitrogenase activity resulting in accepted publications and manuscripts in submission.
• Organized and led a data science team that won consortium authorship in a research publication in Cell Reports Medicine predicting risk for pre-term birth using XGBoost and neural networks.
• My research also won a scientific communication fellowship, grant writing competition, and 3 Minute Thesis competition at SDSU.

Bioinformatics and Data Science Consultant, MedGene Labs
I applied bioinformatics, machine learning, data visualization, web application development, and process automation to support this animal vaccine and therapeutic company.
• Built a user-friendly app that sped-up diagnostic sequence alignments ~ 10x and limits downside risk of data duplication and human error that were present in prior approaches.
• Sped-up DNA construct design about ~10x by programming the multi-step manual procedure into a reproducible automated workflow.
• Developed a Machine Learning model to predict epitope regions on antigens with higher classification performance than competing open-source models like BepiPred2.
• Developed a machine learning model that predicts protein digestion and degradation rates during scaled-up production. Built model that suggests mutations to limit protein loss while maintaining structure and function.

Education

M.Sc., Data Science, South Dakota State University, May 2020 – May 2021 B.Sc., Biochemistry, University of Jamestown, August 2013 – May 2017 Ph.D. Candidate, Molecular Biology, South Dakota State University, defense Nov 2023

Relevant skills

• Prioritizing projects on value/risk tradeoffs
• Scaling explanations between technical and business levels
• Mentoring and training
• Innovation with emerging technology such as GPT

• R, Python, LINUX, SQL, cloud platforms (GCP, AWS)
• MLOps via swagger API’s, Docker, and Shiny Apps.
• Robust evaluation and comparison of models
• Broad modeling repertoire (Regression, Classification, Forecasting, Interpretable ML)

Interests

In college I played football and enjoyed outdoor activities unique to the Dakotas. I now enjoy skiing, hiking, fly-fishing, rugby, soccer, and strategy games.