Data Scientist - Multiple Levels (TS/SCI with Poly Required)

A little about us:
The Red Alpha Data Science practice grew out of Red Alpha's reputation in Software and System Engineering with our Department of Defense clients. As sometimes happens, customers who trust our expertise in adjacent areas asked Red Alpha to assist with some of their burgeoning Data Science problems.
Culturally, it probably suffices to say that we take our work seriously, but not ourselves. Our leaders have spent time in the trenches and have cursed daylight savings time changes and trailing whitespace as many times as you have. We like to say that we spend 80% of our time cleaning the data...and 20% of our time complaining about cleaning the data. Joking aside, our voices matter, and it is easy to see how our decisions affect the Data Science practice and Red Alpha as a whole. We have a clear vision of where we are headed.
Our team takes a pragmatic approach to Data Science, defining it loosely as the intersection of technical expertise, business acumen, and soft skills to solve business problems with data. We spend a lot of time trying to understand the problem before we set about building a solution, and we prefer lower tech useful solutions over shiny algorithms and dust on the shelf. Did we mention we’re pragmatic? We have a diverse set of skills across our team, and whether you are a traditional Data Scientist (whatever that means…), an Applied Research Mathematician, a Database Engineer, a Full Stack Developer, or something else in that neighborhood, if you have a knack for picking apart data to make sense of it, we would enjoy having a conversation with you.
A day in the life:
You’ll likely start your day at a customer location near our Red Alpha offices, checking to see if there are any important items that have just come up. If not, you’ll move on to improving a dashboard or other data product that you’re responsible for, or perhaps produce a weekly output the customer is expecting. You’ll be using R or Python a lot, but also Git, Mongo, some commercial software and several other usual suspects. Once a week you’ll attend a team meeting (with 4 others) to prioritize things, and you’ll have a lot of interaction with the team otherwise, typically several times per day. You’ll need your sense of humor and need to be willing to leave your ego at the door.
What you bring to the table:
Now on to the fun of formal requirements - we have to apologize in advance for the corporate-speak here, but just hold your breath for a few lines and everything will be okay. We are actively hiring data professionals for roles across a broad range of skill levels and projects. Our goal is to find the best fit for you so please note that if you apply to one of them and we see a fit elsewhere we will let you know. So do not worry about applying initially for every position you might be interested in.
All of our data scientists need the following skills:
Proficiency with a scripting language such as R or Python
Experience with data science techniques and algorithms such as classification, clustering, random forests, deterministic forests (jk), hierarchical modeling, deep learning, Markov Chain Monte Carlo, and others. Note that you do not need to have all of these (we hope you enjoyed our random smattering of techniques…!) but you should be comfortable and capable with several of them and know some others not on this list.
A B.S. Degree in Data Science, Mathematics, Computer Science or related field.
For entry-level data scientists, 0-3 years of experience on Data Science projects.