2017 NCAA Tournament Round of 64 Upset Predictions

The Cabri Group / CAN Machine Learning Lower Seed Win Prediction tool has made its first round forecast! Without further ado:

East Tennessee St. (13) over Florida (4)
Xavier (11) over Maryland (6)
Vermont (13) over Purdue (4)
Florida Gulf Coast (14) over Florida St. (3)
Nevada (12) over Iowa St. (5)
Rhode Island (11) over Creighton (6)
Wichita St. (10) over Dayton (7)

 
* If the last play in games add another predicted upset, we’ll update that prior to the game starting.

Update: USC (11) over SMU (6)

One of the obvious observations on the predictions is: “Wait, no 8/9 upsets????” Remember these games show the most similar characteristics of the largest historic collection of upsets. This doesn’t mean that there will be no upsets as 8/9 nor that all of the predictions above will hit (remember we are going for 47% upsets) nor that all games not listed will have the favorites win. The games on the list are there because they share the most characteristics with historic times when the lower seed won.
Also, one of the key team members on this project, Matt, is a big Creighton fan (and grad). He was not happy to see Creighton on the list. I’ll speak to that one specifically. In the technical notes, I indicated that one of the many criteria that is being used is was Defensive Efficiency (DE). Machine Learning algorithm (Evolutionary Analysis) doesn’t like it when the lower seed has a large gap of DE between the lower seed and the higher seed. Creighton actually has a lower Defensive Efficiency than Rhode Island. Sorry Matt. Again, it doesn’t mean Creighton won’t win, it only means that the Rhode Island v. Creighton game shares more criteria with a the largest collection of historic upsets than the other games in the tournament.
As we indicated, we will use the odds as well as a count of upsets to determine how well we do as the tournament goes on. We’ll have a new set of predictions on Saturday for the next round of the tournament and a recap coming on Monday.
For more information about how we created the Machine Learning algorithm and how we are keeping score, you may read the Machine Learning article here:
http://can2013.wpengine.com/machine-learning-basketball-methodology

March 15, 2017 News No comments yet
machine learning prediction

March Machine Learning Mayhem

Machine Learning and the NCAA Men’s Basketball Tournament Methodology

 <<This article is meant to be the technical document following the above article. Please read the following article before continuing.>>

“The past may not be the best predictor of the future, but it is really the only tool we have”

 
Before we delve into the “how” of the methodology, it is important to understand “what” we were going for: A set of characteristics that would indicate that a lower seed would win. We use machine learning to look through a large collection of characteristics and it finds a result set of characteristics that maximizes the number of lower seed wins while simultaneously minimizing lower seed losses. We then apply the result set as a filter to new games. The new games that make it through the filter are predicted as more likely to have the lower seed win. What we have achieved is a set of criteria that are most predictive of a lower seed winning.
 
This result set is fundamentally different than an approach trying to determine the results of all new games whereby an attempt is made to find result set that would apply to all new games. There is a level of complexity and ambiguity with a universal model which is another discussion entirely. By focusing in on one result set (lower seed win) we can get a result that is more predictive than attempting to predict all games.
 
This type of predictive result set has great applications in business. What is the combination of characteristics that best predict a repeat customer? What is the combination of characteristics that best predict a more profitable customer? What is the combination of characteristics that best predict an on time delivery? This is different from just trying to forecast a demand by using a demand signal combined with additional data to help forecast. Think of it as the difference between a stock picker that picks stocks most likely to rise vs. forecasting how far up or down a specific stock will go. The former is key for choosing stocks the later for rating stocks you already own.
 
One of the reasons we chose “lower seed wins” is that there is an opportunity in almost all games played in the NCAA tournament for there to be a data point. There are several games where identical seeds play. Most notably, the first four games do involve identical seeds and the final four can possibly have identical seeds. However, that still gives us roughly 60 or so games a year. The more data we have, the better predictions we get.
 
The second needed item is more characteristics. For our lower seed win we had >200 different characteristics for years 2012-2015. We used the difference between the characteristics of the two teams as the selection. We could have used the absolute characteristics for both teams as well. As the analysis is executed, if a characteristic is un-needed it is ignored. What the ML creates is a combination of characteristics. We call our tool, “Evolutionary Analysis”. It works by adjusting the combinations in an ever improving manner to get result. There is a little more in the logic that allows for other aspects of optimization, but the core of Evolutionary Analysis is finding a result set.
The result set was then used as a filter on 2016 to confirm that the result is predictive. It is possible that the result set from 2012-2015 doesn’t actually predict 2016 results. Our current result set as a filter on 2016 data had 47% underdog wins vs. the overall population. The historic average is 26% lower seed wins and randomly, the 47% underdog win result could happen about 3.4% of the time. Our current result is therefore highly probable as a predictive filter.
 
The last step in the process is to look at those filter criteria that have been chosen and to check to see if they are believable. For example, one of the criteria that was Defensive Efficiency Rank. Evolutionary Analysis chose a lower limit of … well it set a lower limit, let’s just say that. This makes sense, if a lower seed has a defense that is ranked so far inferior to the higher seed, it is unlikely to prevail. A counter example is that the number of blocks per game was not a criteria that was chosen. In fact, most of the >200 criteria were not used, but that handful of around ten criteria set the filter that chooses a population of games that is more likely to contain a lower seed winning.
 
And that is one of the powerful aspects of this type of analysis, you don’t get the one key driver, or even two metrics that have a correlation. You get a whole set of filters that points to a collection of results that deviates from the “normal”.
 
Please join us as we test our result set this year. We’ll see if we get around 47%. Should be interesting!
 
If you have questions on this type of analysis or machine learning in general, please don’t hesitate to contact Gordon Summers of Cabri Group (Gordon.Summers@CabriGroup.com) or Nate Watson at CAN (nate@canworksmart.com).
**Disclaimer: Any handicapping sports odds information contained herein is for entertainment purposes only. Neither CAN nor Cabri Group condone using this information to contravene any law or statute; it’s up to you to determine whether gambling is legal in your jurisdiction. This information is not associated with nor is it endorsed by any professional or collegiate league, association or team. Machine Learning can be done by anyone, but is done best with professional guidance.
 
 
 

March 13, 2017 News 7 comments

Predicting the upsets for the NCAA Men’s Basketball Tournament using machine learning

Contemporary Analysis (CAN) and Cabri Group and have teamed up to use Machine Learning to predict the upsets for the NCAA Men’s Basketball Tournament. By demonstrating the power of ML through our results, we believe more people can give direction to their ML projects.
 
Machine Learning (ML) is a powerful technology and many companies rightly guess that they need to begin to leverage ML. Because there are so few successful ML people and projects to learn from, there is a gap between desire and direction. 
 
We will be publishing a selection of games in the 2017 NCAA Men’s Basketball Tournament. Our prediction tool estimates games where the lower seed has a better than average chance of winning against the higher seed. We will predict about 16 games from various rounds of the tournament. The historical baseline for lower seeds winning is 26%. Our current model predicted 16 upsets for the 2016 tournament. We were correct in 7 of them (47%), which in simulated gambling gave the simulated gambler an ROI was 10% (because of the odds). Our target for the 2017 tournament will be to get 48% right.
 
Remember, our analysis isn’t to support gambling, but to prove the ability of ML. However, we will be keeping score with virtual dollars. We will be “betting” on the lower seed to win. We aren’t taking into consideration the odds in our decisions, only using them to help score our results.
 
We will be publishing our first games on Wednesday 15th after the first four games are played. We won’t have any selections for the first four games as they are played by teams with identical seeds. Prior to each round, we will publish all games that our tool thinks have the best chance of the lower seed winning. We’ll also publish weekly re-caps with comments on how well our predictions are doing.
 
Understand the technique that finds a group of winners (or losers) in NCAA data can be used on any metric. Our goal is to open up people’s minds onto the possibilities of leveraging Machine Learning for their businesses. If we can predict things as seemingly complex as a basketball tournament (Something that has never been correctly predicted), then imagine what we could do with your data that drives your decisions?
 
If you have questions on this type of analysis or machine learning in general, please don’t hesitate to contact Gordon Summers of Cabri Group (Gordon.Summers@CabriGroup.com) or Nate Watson at CAN (nate@canworksmart.com).
 
Those interested in the detailed description of our analysis methodology can read the technical version of the article found here.
**Disclaimer: Any handicapping sports odds information contained herein is for entertainment purposes only. Neither CAN nor Cabri Group condone using this information to contravene any law or statute; it’s up to you to determine whether gambling is legal in your jurisdiction. This information is not associated with nor is it endorsed by any professional or collegiate league, association or team. Machine Learning can be done by anyone, but is done best with professional guidance.

March 13, 2017 News 3 comments

Why you should invest in your employees

Investing in employees means more than just treating them well by giving them benefits and a flexible schedule. It means putting time and resources into individuals who have potential for greatness, but may need a little guidance.

More and more companies find it strategic to invest in their employees, even if it means allowing them to move on to greener pastures when the time comes. These companies care about the personal and career growth of their individual employees. They don’t back away from new talent who may need a little training. They see opportunity in new hires, unlike most who are afraid to waste money on training.

One such company is Contemporary Analysis (CAN). CAN provides predictive analytics to businesses with needs to unlock patterns in their data. President Nate Watson committed to investing in employees from the beginning. The result of his investments is the substantive alumni network of CAN.

Alumni networks are the metaphorical badges of honor for companies committed to their employees’ growth. Employees may move on to other businesses or start their own companies, but they continue to maintain positive relationships with companies like CAN who invested so much in their future. Past employees reach out to the network for advice, giving them a makeshift peer network even when they are the only data scientist in their company.

Watson comments, “By investing in our employees’ future, we get people not only willing to go the extra mile for us, but access to employees who have the tenacity to figure out how to solve problems. We lose really good talent, but a lot of the contracts we have right now are from companies that have our former employees in them.”

The opening comic portrays the decision to invest in employees perfectly: CFO: “What if we train them and they leave?” CEO: “What if we don’t…and they stay?” Companies like CAN know that whether employees stay or leave, resources are not wasted.

CAN doesn’t wait for the most highly trained analysts to walk through the door. CAN nurtures potential with patience for greatness.

Some companies make the switch to investing in their employees years after they begin operations. For CAN, commitment to training employees is embedded in their DNA.

Nine years ago, the same people who rely on data analysis to keep their companies moving today had never even thought of hiring a data analyst. This was during the recession. As a result, a lot of creative people with experience in data science were out of work.

Grant Stanley is the former CEO of CAN. Stanley saw the small pool of highly driven, intelligent, but under employed people as something special–something with which he could start a company.  

He then built CAN, the now 9 year old predictive analytics company. Stanley and his non-traditional co-workers would approach companies like Mutual of Omaha or West and say, “Give us the hardest problem you have and let us have a crack at solving it.” Many times they would get an opportunity because a solution was already tried by the “regulars” and failed. Giving CAN a chance provided no risk and very little outlay of cash.

Stanley is now CEO of Bric, a software company originally designed for small companies that now helps Fortune 500 companies plan and project manage using predictive analytics.

CAN approached the solution differently even from day one. Many times it felt like crash a course in learning the models necessary to succeed. However, CAN’s employees were already good at learning. They looked for solutions, sometimes in other verticals and industries, and applied that knowledge back to the original problem.

They found success even though they were younger and less funded than some of their well known competition. This problem solving ideology has become a cornerstone of how CAN does business, even to this day.

After years of solving difficult problem after difficult problem, the young data scientists were well trained. They now had resumes to qualify for more prestigious positions, even CEO or management positions. CAN learned to cope with employees moving on. They started an alumni network to capture the excitement of the “graduation” of the employees. The alumni network now boasts 15 former employees in 13 companies.

Nate Watson maintains the same mindset of investing in employees for CAN today.

If you look at CAN now, Watson has changed very little. Yes, CAN today has more resources and more consistent work, but their motto still reflects their passion to make businesses better: “Empower the great to build something greater.” This is not only true for how they work with clients, but also how they treat their employees. They aren’t afraid to smile and wave goodbye as their best employees seek other opportunities. That’s why they have such a strong alumni network.

In fact, last month CAN announced the start of the Omaha Data Science Academy (ODSA), the ultimate goal of which is to train and place a data scientists in every company in Omaha. Their goal is not to replace the four year degree, but provide training for those who need an extra push before they become entry-level data scientists.The DSA’s motto of “Building Smarter Talent” likens back to Watson and CAN’s original mantra. 

Further on down the road, of course, there will be an alumni network for the Data Science Academy. Watson comments, “We want each cohort to be able to connect every cohort as they move between companies and up in each company. Having that peer network is going to be key to the success of graduates.”

Watson hopes that the ODSA will inspire even more businesses to invest in their employees.

–The ODSA’s Alpha class starts September 19th–the graduation party, already RSVP’d by some of the most forward thinking companies in the surrounding area, will be on December 8th.

CAN isn’t the only business with an impressive alumni network. Strong alumni networks like CAN’s, however, do seem unique to the tech world.

Aron Filbert at Lyconic is proof of this. Lyconic provides software designed to improve security guard management. Lyconic’s products are proven to increase accountability and decrease turnover rates among guards. 

Filbert needed a talented software developer to create Lyconic. However, he knew that he could not compete with the corporate world in pay. He gave his employees other perks to make up for this, like casual dress, time off, a lax work schedule, fluidity in moving up at Lyconic, and so on.

These other perks, in particular Filbert’s commitment to train and grow with his employees, helped Lyconic build a strong alumni network. Filbert’s greatest success story is a man named Carl Zulauf. He worked for Lyconic for a little over one year, gaining valuable experience alongside Filbert.

He moved on to a start-up company in New York City where he was compensated very well.

Filbert speaks of Zulauf with pride, not resentment. This is what makes Lyconic and CAN’s alumni network unique to growing tech world: they believe in each other, and that leads to success for everyone. Most businesses only care about the success of their company, not the growth of individuals.

Three significant results of having an alumni network.

Companies use their alumni networks in different ways. Since the beginning, CAN noted three positive and long-lasting results for their alumni network.

The physical result: CAN’s alumni network includes some of the biggest names in Omaha as well as some of the most promising startups. Names like HDR, Avantas, TD Ameritrade, Kiewit, Flywheel, and even Ebay are sporting former CAN employees. As well as 4 founders of data science software companies: Eric Burns at GazellaWifi, Luis Lopez at Crumb, Grant Stanley at Bric, and James Rolfsen at Kojuba. With each movement out, a vacancy occurs that can be filled is filled by new employees that need only training and an opportunity.

The social impact: The alumni network is an active and working peer connection hub. Former employees of CAN left on positive terms which means they still keep in touch and occasionally ask for help on projects or advice on business moves. The reverse is true as well: CAN doesn’t hesitate to reach out to the alumni network with questions and advice.

The emotional fulfillment: As Watson continues to invest in his employees and sees other companies like Lyconic do the same, he feels a deep sense of pride for the community CAN helped build.

As more and more companies make the switch to investing in employees, what does the future of business look like?

Investing in employees allows companies to retain the value of each individual, even after they are gone. As more businesses decide that money spent on staff training is money well spent, their pool of resources grows beyond their own company. Their former employees continue to have value in an alumni network. In a sense, by investing in employees you never really have to cut the cord when they move on from your business. This doesn’t get rid of competitiveness between companies completely, but it does allow different businesses to act as support for each other’s growth.

Watson of CAN ends with this note, “Investing in people will always be my personal mantra. I hope it continues to permeates the atmosphere of CAN, the ODSA, and the Data Science Community long after I’m gone.”

You learn. You move up. You let go. You come back and talk shop. You train someone else. You maintain connections and continue to encourage each other. That’s CAN. That’s the ODSA. That Lyconic. That’s all companies committed to individual achievement. That’s the beauty of investing in employees.

August 8, 2016 Featured Posts, News 5 comments

Rethinking Business Intelligence Software

People don’t care about business intelligence software, they care about what it can do for them.  CAN is built on this idea.  Instead of focusing on business intelligence software, we are focused on providing answers directly to our clients.  We are improving this process by launching the CAN Portal.  The Portal is how we work with our clients.  It will allow you to get better answers faster and more securely.
What are your objectives?
(more…)

September 17, 2012 News No comments yet

What does a CEO do?

I overheard someone the other day telling their friend that there was no way their CEO deserved a million dollars a year.  “What does our CEO even do anyway, ” she said?  “I wish I could come in late, play golf all day, and have no responsibilities.  I would do the job for $500,000 and do it better than him…”
I wish I could say I turned and scolded her about how her CEO probably was at a networking event while she was with her family, works most weekends, including holidays, and never shuts off the pressure of running a business, but truth be known, I didn’t know if that was the answer. (more…)

August 14, 2012 News No comments yet

New Addition to the CAN Offices

In the past 7 months Contemporary Analysis has expanded from 4 to 20 employees, and will easily expand past 50 employees within the next year.  To accommodate that growth we have had to expand out offices at 1209 Harney St.  Here is an updated look at our offices. (more…)

August 9, 2012 News No comments yet

Tadd and Jefferson go Mining for Data in Wyoming

CAN is helping one of our clients improve their asset management strategy, by building predictive models to determine when heavy equipment is most likely to fail.
CAN’s asset management models will allow our client save hundreds of thousands of dollars each year, by converting emergency repairs into scheduled maintenance.  Imagine the money and time that can be saved if repairs can be preemptively made in several hours instead of the weeks or months it takes to make repairs in the field.
While we could have developed the model from our offices in the Old Market, we needed to make sure that we understood the conditions on the ground. Jefferson and Tadd decided to take a trip to Wyoming and spend a week learning about the machines and interviewing the experts that use the equipment on a daily basis.
Their goal was to make sure that we had political support from the people that were going to use our models, and that we could build balanced models that combine data, theory and math.  The following are some of the photos from their trip.  I hope you enjoy.

Mining for Data






We might push paper for a living, but we love to get our hands dirty to build beautiful models and to understand your business! Please contact us to learn how we can help you.

January 30, 2012 News 6 comments

Why I work at Contemporary Analysis

I get asked why in the prime of my career I went back to working for a startup company, run by young talent, in a field on the cutting edge of analytics.  It was because, for the first time, I felt like an owner had a vision I could get behind.  He wanted to be something better, do something different, and wanted me to help him create something magnificent.  I saw it as a unique opportunity because, for the first time, I found a true entrepreneur.
Most people define anyone that starts a business as an entrepreneur, which is actually not accurate.  That definition is the definition of a business owner.  An entrepreneur is a mindset, a way you do business and how you look at problems.  I knew Grant, the CEO of Contemporary Analysis, was different when he told me he was going to turn down being bought by 2 different companies. That alone puts you in a different class.  Most owners would sell if they ever got the chance.  In fact, Grant has no intention selling off or IPO’ing his  juggernaut of a company.  In fact, he wants to be a privately owned Fortune 500 company headquartered in Omaha, NE.  Grant not only is an entrepreneur, but he has vision.  And big vision at that.
People have told us that we can’t do it, and yet, we keep doing it.  We will easily have over $3 million in revenue for 2012, double that of last year and 10x’s that of 3 years ago, and already have contracts with 2 of the Fortune 500’s in our hometown.  We are hiring, building out, releasing new products, and thinking about how to do business better.  I have worked at a couple of startups in my life, but this is the first that does that kind of reflection and planning.  Our goal is to not only grow, but grow in a way that is sustainable and scalable by taking the time and energy to do things right the first time.  We want to build our products, people, systems and processes so they last, instead of being obsolete the next year.  While this requires extra time to research, tinker and think about what the future will look like, this philosophy allows CAN to grow without having to look back.  I wanted to be part of a company that has that kind of philosophy.
This philosophy has appealed to me.  I used to think I needed all the answers before I could recommend change.  Through the books Grant, he wants us to grow as humans and executives, has given me to read, I realized that I didn’t need all the answers before tackling a problem.  In fact, our whole company is based on the fact the answers that are out there are not the best way any more.  We have to invent new ways to stay ahead of competition or risk being a follower.  That understanding changed what I defined an entrepreneur as.  No longer did I see it as someone who likes risk, who lives on the stress created by it, and who loves the idea that while he or she may fail, the reward for winning is enormous.  Instead, I began to see an entrepreneur as someone who isn’t willing to accept things as they are as the best way.  In the hands of a true entrepreneur business is the best platform to change the world.
My philosophy also changed how I viewed risk and how I found that entrepreneurs viewed risk.
In the book Breakthrough Entrepreneurship, Harvard Business Professor Howard Stevenson defines entrepreneurship as “the pursuit of opportunity without regard to resources currently controlled.”  From working with Grant, I know that this is true.  He has the unique ability to take action that require using resources that he doesn’t have and sometimes that don’t even exist.  For example, he founded Contemporary Analysis in 2008 well before you could even Google “big data”, “data science” or “predictive analytics”.
Also in the same book, Jon Burgstone, summarizes a true entrepreneur’s ideology:

Every time you want to make any important decision, there are two possible courses of action. You can look at the array of choices that present themselves, pick the best available option and try to make it fit. Or, you can do what the true entrepreneur does: Figure out the best conceivable option and then make it available.

This is what makes Contemporary Analysis great, our leader does not look to see what choices are available, instead he looks for the option that would be best for the business, and then goes and finds out how to make that option available.  This takes leadership that is empowered, and empowers everyone they work with to question why everyone has always done everything.
For example, we go directly to buyers and  talk only to people who have the need,willingness, and resources to buy what we sell.  Also, we research every decision we make from chairs and desks to computers and phone systems.  We find the system that makes sense to us and then find the vendor who sells it.  We certainly don’t wait for cold calls, don’t put up with bad customer service, or buy from poorly trained sales people.  We do things differently.
I am excited to work here.  I have no pedigree of how things have been done for years to try and get out from under.  I have the freedom to help my clients answer the questions that they have been struggling to answer for years, and help them make better decisions on how to make their businesses succeed.  At CAN I am not limited by the technology or resources available, but empowered by the mandate to help every client work smart.
One more quote from Professor Stevenson:

When you don’t have the cash to boss people around, like in a corporation, you have to create a more horizontal organization. “You hire people who want what you have and not what you don’t have.”  In other words, entrepreneurs offer their team members a larger share of a vision for a future payoff, rather than a smaller share of the meager resources at hand. Opportunity is the only real resource you have.

And this place is one of the best opportunities to make a difference in the world I have ever seen.
That’s why I work here.

January 16, 2012 News No comments yet

Job Board: Contemporary Analysis Navigator

Contemporary Analysis is a global data science company based in Omaha, NE that provide predictive analytics to multiple Fortune 500 companies and small businesses in the United States, Europe and Asia.  Contemporary Analysis is focused on making analytics accessible to companies of all sizes and industries, and offers standard products and professional services.
Contemporary Analysis (CAN) Navigators are the core of CAN’s client experience.  They are responsible for helping customers achieve their business goals by helping them discover, understand and use CAN’s business systems.  This is the perfect position for someone that loves to continually learn and teach others.  Navigators are responsible for learning about client’s businesses and their goals, researching and helping them develop and implement a plan to help them Work Smart.  They must be experts in both the technology and customer service.
Primary Responsibilities:
Introduce: CAN Navigators are responsible for introducing clients to CAN to determine if they have a need, willingness and resources to purchase CAN’s systems.  This includes handling incoming clients from CAN’s website and referral program, and also contacting sales leads qualified with CAN’s Tracker system.
Discover: Once a client has been introduced to CAN, and has expressed the need, willingness, and resources CAN Navigators will train to be experts at data science and business so that they can work with clients to understand their business and their business goals.  Clients should be confident that their Navigator has a good understanding of their business.  If a Navigator is not sure that they fully understand a client’s business, their business goals, or the right solution they will research until they understand more.
Create: After the Discover stage, Navigators will work with Client to create a plan to help them achieve their business goals.  This will include discussing CAN’s systems, as well as third party products and services.  The purpose of this step is to collaborate with clients to create a timeline and action plan to work smart.
Work: After the Create stage, Navigators will work with Clients to setup and understand CAN’s systems in their businesses, and then work with each client to help them achieve their business goals.
Relevant Experience and Education

  • Minimum Education: Bachelor’s Degree from an accredited institution, with a degree in business or relevant work experience.
  • Able to maintain focus in a high charged environment and manage competing priorities.  This includes experience managing multiple projects simultaneously against tight deadlines.
  • Experience solving business issues with the consultative application of advanced analytics and/or information technology.
  • Strong presentation and client management skills, up to the Executive level.  This includes being able to explain highly detailed and technical subject matter to non-technical audience, and being able to present and sell analytical concepts to clients.
  • Experience delivering insight to internal or external clients by building on a technical foundation that includes a conceptual understanding of modeling techniques and a basic grasp of statistics.  Ability to build analytical applications to solve a practical problem, in an on the spot high-pressure situation.
  • Experience in project management and managing a team to meet a deadline, manage client expectations, and maximize client satisfaction relative to solution profitability.
  • Functional experience in one or more of the following areas: selling analytic services, project management, analytic product development, pre-sales, implementation, account management
  • Technical foundation to include one or more of the following areas: Bayesian statistics, multiple regression analysis, econometric modeling

If you are interested in learning more and applying contact Grant Stanley by phone at 866-963-6941 #801 or connect with him on LinkedIn.  Please have your LinkedIn profile up to date before applying.

December 13, 2011 News No comments yet
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