Robert Nendorf

About Robert Nendorf

I believe that by connecting human experience with science and mathematics we can transform business and society. What motivates me is leading the cultural, organizational, and technical change within a company to help it think differently about how it make decisions and innovates by driving the adoption of data science and empirical experiments. During my career I have— led a data science / data engineering org that solved deep problems in sensor fusion and the detection of driving behaviors and accidents across 40M+ mobile devices and 1.5T miles of driving data— led a product / engineering team responsible for developing a suite of ML deployment products across a large enterprise— managed a recruiting process that brought in over 120 data scientists in two years, and helped mature an advanced analytics team— taken on roles with increasing scope and responsibility for data science projects across insurance underwriting, claims, and other functions— studied and used algebraic topology in both academic and industry settings.My background prior to data science is in mathematical research and teaching. I enjoyed the many aspects of this pursuit: independent and collaborative research, attending seminars and conferences, teaching courses and helping students. At the time, I didn\'t realize how well this experience would translate to industry data science roles. Teaching improved my communication skills with non-technical audiences, and required leadership skills such as providing feedback. Research developed my ability to self-motivate, think differently, and confront challenging and complex technical concepts.I am always interested in making new professional connections. Reach out if you want to talk machine learning, math, or sailing.Strengths: thinking differently, driving results, leadership, data science & ML, coaching, communication, Agile, insurance telematics.

Experience

1. SVP, Head of Data & Analytics

   GM Financial

   Apr 2024 — Present

   At General Motors Insurance we are on a mission to be the auto insurer of choice for GM vehicle owners. We are achieving this through key strategic advantages- acquiring customers via GM touch points to keep acquisition costs down -> lower prices- leveraging the GM ecosystem to build unique premium customer experiences -> higher quality- using data with privacy-first practices to create world class sophistication -> profitabilityMy role is to lead all strategy & execution for data, analytics, and AI for the insurance business within GM. We are creating a modern cloud data platform with rigorous governance that supports self-service analytics across domains. On top of this we are building a suite of ML and AI capabilities that will optimize operations across the business to maximize our customer lifetime value and minimize our operational expenses. I spend significant time working as a part of the leadership team at GM Insurance strategizing how to make our vision a reality. We are a great cross-functional team where people weigh in and debate without being confined to \"your lane\". I also love the opportunity to build great technical teams and we have successfully recruited and retained a fantastic group of people who go above and beyond in order to make GM Insurance successful.We have no shortage of deep challenges creating a new insurance business from scratch. This makes it all the more meaningful as we 10x our business while using AI to ensure our costs do not scale with that growth.

Education

• Northwestern University

  M.S., Mathematics

• Western Michigan University

  B.A., Mathematics

• Northwestern University

  Doctor of Philosophy (PhD), Mathematics

Skills

• Machine Learning
• Statistical Modeling
• Calculus
• Communication
• Quantitative Analytics
• Research
• C++
• Theory
• Predictive Modeling
• Python
• Mathematical Analysis
• Curriculum Development
• Teaching
• Tutoring
• Data Mining
• Applied Mathematics
• Science
• Leadership
• University Teaching
• Mathematics
• Mathematical Modeling
• Higher Education
• Product Development
• Analytics
• R
• Algorithms
• Neural Networks
• Programming
• Linear Algebra
• Agile Methodologies
• Statistics
• Data Analysis