•Founded first BU Data Science team with 9 Data Scientists. 4 ML Ops Engineers and 2 Data Engineers were also part of the team until that function centralized. Half of team is spread throughout United States and half in Pune, India. Mentored team members on best practices in machine learning and data engineering, fostering an environment of continuous learning and innovation.•Developed automated pipeline using data inputs such as on prem BI, Snowflake data products, and third-party data into a Feature Store; AWS cloud infrastructure for model experimenting and publishing; and Salesforce (CDP, Data Cloud, CRM, MC), Power BI, and Snowflake output.•Created a suite of models for Marketing Campaigns using propensity, segmentation, and other logic for optimized return. Used A/B testing for effectiveness. On track to meet goal of +$361MM revenue per year over next 3 years.•Developed and managed KPIs and OKRs, reporting directly to senior leaders with reports also seen by the board such as increasing accuracy of pricing predictions, decreasing lapses, power of customer and contract segmentations, and take-up for new retirement products.•Appointed consultant for Gen AI, LLM, and Natural Language centralized teams to work on retirement use cases.•Developed models for retirement group products such as 401(k) serving marketing, distribution, admin, repricing, and others.•Founded and led Retirement Data Science Steering Committee with key senior level business partners. Defined and implemented long-term analytics roadmaps, aligning data science initiatives with business objectives and ensuring that models and solutions are scalable across the business unit and organization.•On the enterprise data science steering council to advance data science at Principal.•Regularly trained and led Data Scientists, fostering an environment of continuous learning and innovation.•Regularly educated business on ML, Gen AI, bias, fairness, and other data science topics.

• Develop machine learning models using R / SAS / VBA / SQL for competitive pension risk transfer bidding process to measure mortality assumption at an individual level and other risks. Used company experience, zip+4 demographic data (40 million rows, 66 features), and industry data (1 million rows, 30 features). First machine learning model used in actuarial pricing. Regular communication and meetings with department Chief Actuary and Principal Chief Actuary.• Co-developer of production pricing model in R used throughout department.• Use analytics to explain and communicate results to department CFO. Help implement changes to tighten forecast and manage gain/loss account. Help develop explanations for quarterly Principal Earnings Call. Often dashboard results in Power BI / R Shiny.• Founding member of Principal R User Group; taught R at Principal with own 10-hour interactive lecture series. Two iterations with over 30 students each time.• Founding member of Principal Python User Group/Steering Committee. Co-leader of monthly meetings. Over 150 members.

• Managed stochastic valuation model in Matlab used for liability management, pricing, and hedging. Set monthly pricing rates and communicated to marketing leaders, team members, department CFO, and department Chief Actuary.• Co-owner internal Variable Annuity assumptions. Set assumptions at least annually. Used internal and industry data using SAS / VBA / Matlab / SQL / Bloomberg terminal. Expanded internal data use with 14 tables in database for additional features.

Instructor of College Algebra and College Statistics teaching college students who feel that they cannot pass college math. A lead tutor in a public lab. Private tutoring.

• Conducted comprehensive validation of a C++ model using a VBA model to identify gaps forfuture enhancement. Was first to identify weaknesses in both models and provide meaningfulsolutions.• Headed a project that developed, implemented, and enforced quality coding standards toimprove current model transparency.• Presented on topics including: technical coding solutions in VBA; a historical account of whyselect insurance companies faced insolvency and lessons to learn; and understanding trends ofsuccessful and unsuccessful product ideas in recent company history.

A Utah company based in the Wasatch Front.Direct liaison with Corporate Staff, managed all restaurants and relations with local recreation centers.Responsible for hiring and letting go of employees, managed approximately 50 personnel at all times and coordinated employee schedules and shifts. Responsible for booking, transportation of revenue, collections and relations with credit companies.Designed and edited menu, tracked inventory and oversaw loss prevention.Charged with product purchases via online or from local vendors; drove box truck for food product transportation.Planned and conducted training sessions on inventory control, customer relations, register operation, credit card readers and general money handling.