Hi, I'm Nathan

Hello, my name is Nathan Diekema, allow me to introduce myself. I am a recent graduate and aspiring data scientist. I'm particularly interested in the applications of machine learning and AI. My appetite to grow and learn new skills drives my curiosity for topics such as computer vision, deep learning, and data ethics.

I believe that my capabilities as a data scientist are firmly grounded in my education. I recently graduated from California Polytechnic State University, San Luis Obispo with a M.S. in data analytics and a B.S. in electrical engineering with a focus in computer science. As a student at Cal Poly, I was faced with a plethora of challenging technical courses that required me to become a proficient problem-solver, effective communicator, and dependable team member.

My personality also plays a major role in my ability to be an effective data scientist. I am detail-oriented, creative, analytical, and curious. I also love examining and discussing the ethical considerations related to big data, among other things. Even in my personal life, I like to fully analyze the potential outcomes of a situation before making a decision.

During my co-ops with Netflix and AT&T I was able to apply everything I had learned to real-world business problems. I collaborated with a team of students with the goal of coming up with actionable recommendations given hundreds of GBs of data. I was highly involved in the whole process, but my main responsibilities included: data cleaning, deriving meaningful aggregate variables, feature selection, and customer segmentation. It was valuable to have the opportunity to work with data of that magnitude and to be exposed to the simple fact that in practice there's usually not a single clear solution that stands above the rest, it's just a matter of finding the best solution.

Over the past few years, I've gained hands-on experience as a Data Scientist at two fast-paced startups, each with fewer than 50 employees. This environment gave me the unique opportunity to work directly with leadership and cross-functional stakeholders on high-impact projects that shaped company strategy. At Postal, where I was the sole Data Scientist, I built data infrastructure from the ground up—defining company-wide KPIs, automating multi-source data pipelines, developing predictive models to reduce churn, and improving data accessibility through dashboards and reporting. At Pebble, I focused on healthcare analytics, where I implemented and optimized risk-based pricing models, reducing error by 6% and runtime by 80%, while also streamlining operations with automated proposal generation and client-facing dashboards. Across both roles, I've thrived in the fast-paced, collaborative environments that demanded adaptability, self-learning, and the ability to deliver data-driven solutions that directly impacted business outcomes.