My two most recent blog posts were about Scaling Analytical Insights with Python; part 1 can be found here and part 2 can be found here. It has been several months since I wrote those, largely due to the fact that I relocated my family to Seattle to join Amazon in November; I’ve spent most of the time on my primary project determining our global rollout plan and related business intelligence roadmap. ...

If you would like to read Part 1 of this Series, please find it at this link. A fair amount has happened since my Scaling Analytical Insights with Python (Part 1) post back in August. Since that time, I decided to resign from FloSports in order to join Amazon’s Kindle Content Acquisition team as a Sr. Product Manager -- this obviously includes moving my family from Austin, TX to Seattle. While this was taking place behind the scenes, I had every intention to return to my write up of Part 2 of this series. Note that I’ve decided to put Part 3 on hold, and I may potentially not revisit the topic of using Python for financial analysis for a decent while. ...

In recent months, I’ve written about some of the critical undertakings and initiatives which I oversee as VP of Product at FloSports. These have included my efforts to build a data informed culture through product experimentation, our overall approach to our analytics tech stack, and our approach to building and reviewing our rolling financial forecasts. ...

As VP of Product at FloSports, I oversee our data warehouse roadmap and manage a team consisting of a senior product manager, product revenue developers, data engineers, and data analysts. Some of the responsibilities that I really enjoy about this work include working at the forefront of business intelligence, leveraging data and product analytics tools such as Periscope Data, Mode Analytics and Segment, and collaborating with very bright people to drive measurable results and accelerate our growth. ...

In an ongoing attempt to be helpful to and learn from others serving in similar job capacities, I am continuing to review my experiences in building out our data infrastructure over the past ~12 months and discuss the most helpful applications which currently sit within our analytics tech stack. In my most recent post, Data Informed Experiments, I discussed the difference between being “data informed” and being “data driven”. I also provided some perspective related to building a “data informed” organizational culture – my intent is to share what we have learned along the way and discuss the applications which enable us to take advantage of all of the work we’ve completed to-date. ...