I have worked on a number of projects and products that cover a wide variety of different areas.  Below is a list of various projects I have led.  Some of them have details I can share and others I can't due to the project still being live or under NDA. 

Personalization:  I created and led the entire personalization program at Best Buy from the ground up.  This included building the back-end infrastructure, front end look and feel and the data science team to create the algorithms.  Best Buy didn't have any of this in place.  None of the systems for the various platforms spoke to one another.  I created the hadoop platform, a platform they still use today, and a hybrid on-prem and cloud platform to handle data overflows (something they have adopted corporate wide).  This was build in less than 90 days, from concept to product ready.  This platform, when I was at Best Buy, processed more data than the rest of the company combined.  Vendors wanted to charge $20-30 million to build it and take 18-24 months.  We had it built in less than 90 days and for about $3million.  To put this into perspective, some other retailers of similar size, took a year and closer to $8-10 million to do the same.  Here's an article that shows just how important personalization is to Best Buy, it was important enough to Joly to make it part of his turn around strategy.

Artificial Intelligence:  Leader on a project at Target pioneering customer identification, even when not logged in.  Target spends a lot of money on ads.  I pulled a team together that could analyze the data of various platforms and understand the devices of customers who where not logged in.  The idea wasn't to figure out who they were, but which were the most used devices and focus ad spending on activities on those devices, which could save Target millions.  We had an 86% accuracy.  At the time the best anyone else could do was 30%.  If you want more details, here's a story that can help.

Machine Learning:  Used machine learning at a variety of jobs and clients for various projects like pricing, optimization and recommendation engines.  Machine learning is a mainstay of analysis now.  I have used it on products such as recommender systems, pricing models, predicting the weather impact, etc...  I also know the pitfalls of using machine learning and I'm well versed in patent law issues along with security and data governance around machine learning which is something you won't find with most people in this space.   Depending on the type of recommender being built, you could see a 5-7% lift on sales on the low end or a 15-20% lift, after a stabilization period.  Recommenders can be an inexpensive way to increase sales, if done right.  If not done right, they are a money pit and a potential source of lawsuits.  Here's a link to one such project I put together for CH Robinson where I was their Advisor for big data and data science for a year.  Again, started with nothing and built a great team and products that allowed them to help Microsoft and others.

Deep Learning:  Used deep learning around facial recognition for in-store experiences, helping to better target our mobile messaging with the image recognition data.  There is a lot of potential here around the in-store or in general customer experience.  I would love to work on more projects, it is just finding a company willing to invest.  Since most in deep learning are expensive, few companies are willing to invest, but the payout would be worth it.

Data Science:  Built of the first production level data science teams in retail and the Midwest.  Changed how retailers analyze data forever with this project.  I have also built several teams, hiring the data scientists, developers, designers and engineers.  Back in 2011 when few even knew the term data science or big data.  I launched a team at Best Buy.  The model we created was replicated my many other companies in the Midwest and is still a model used today.  I then went on to build teams at Target and CH Robinson.  I get asked a lot to help companies build their data science practices.  Few can build a team and make money, I have been able to build the team and make money.  I build high performing teams that focus on business results.  You don't need an army, you just need the right leadership and processes in place to get the results.

Big Data:  Built of the first production hadoop clusters in retail and the Midwest.  Changing the way retailers do business and analysis forever.  I have hired several teams around this practice at a number of companies.  Back in 2011, hadoop was pretty much just in Silicon Valley, very few production clusters were running outside the valley.  We created one in Minnesota.  I did a lot of work to recruit and build a community locally around it.

Data Analytics:  Early adopter of Spark (2013), helping to pioneer how many are using it today.  Spark was created in 2012, most companies jumped onboard in late 2014.  I have always been very good at predicting what tech will take off and investing in them early to capture the advantages of using them before others.

Augmented Reality:  Created an app provided product information on the app when viewed.

Virtual Reality:  Experimented with various tools to help improve analytics productivity with VR.

Blockchain:  I worked on a project to deliver a blockchain solution to a distributor that wanted to better track inventory. 

Data Governance:  Created and took part in the creation of several data governance programs.

Data Strategy:  Built the data strategy practice for several large and small companies.

Data Leadership:  I manage and lead data teams and products.  These teams can have budgets from a few million to the $10M plus range.  It is also a mixture of roles such as developers, data scientists, engineers, UX, BA's project managers and DBA's.