Kristen Kehrer Tech Guru

Kristen is #8 LinkedIn Global Top Voice 2018 – Data Science & Analytics. She started her journey to becoming a Data Scientist without knowing it. She finished a BS in Mathematics in 2004 but wasn’t sure how she would be applying this education in industry. She then returned to academia for a Master’s Degree in Statistics, and knew that she wanted to be analyzing data, building models, and helping to guide business decisions. Since 2010, she has utilized Data Science across multiple industries, including the utilities, healthcare, and eCommerce. Prior to attaining her Master’s Degree, she was a high school math teacher, and have always enjoyed tutoring, coaching, and mentoring. Her passion is leveraging her experience with the end-to-end job search for Data Science positions to help others effectively market their skills to land a job in this field. The coaching she has personally received was invaluable to how she approached the job search and how she presented herself, she is so thrilled to share this expertise with others. Kristen is also a member of the Data Science Office Hours YouTube channel. When she is not doing Data Science, she can be found with her husband and two young children. She is also a mechanical keyboard hobbyist and aerialist.


Effective Data Science Presentations

If you’re new to the field of Data Science, I wanted to offer some tips on how to transition from presentations you gave in academia to creating effective presentations for industry.


Getting into Data Science FAQs

I often see similar questions in my inbox or asked in webinars. I’d like to set the record straight with data. However, I didn’t need to start from scratch, there was an excellent article on KD Nuggets by Jeff Hale. Here is the link to his article: “The Most in Demand Skills for Data Scientists”. He had already scoured multiple job search site and aggregated data on the demand for different skills for data scientist. I recreated some of his analysis myself, so that I could come up with some points for this article, and to make sure his numbers matched mine before posting. The charts I created below are based on data from searches on only.


Setting Your Hypothesis Test Up For Success

Setting up your hypothesis test for success as a data scientist is critical. I want to go deep with you on exactly how I work with stakeholders ahead of launching a test. This step is crucial to make sure that once a test is done running, we’ll actually be able to analyze it. This includes:


Things to Think About When A/B Testing

I’ve spent the last 6 years of my life heavily involved in A/B testing, and other testing methodologies. Whether it was the performance of an email campaign to drive health outcomes, product changes, Website changes, the example list goes on. A few of these tests have been full factorial MVT tests (my favorite). 


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