Senior Data Scientist (TS/SCI with Polygraph)

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:We are looking for a Senior Data Science to join a team to develop and maintain robust and scalable data pipelines for enterprise AI applications. Responsibilities include:
Develop high-perforamnce data parsersfor extremely large and complex datasets
Implement and manage various database systems, including graph, SQL, NoSQL, and vector databases
Collaborate with AI/ML engineers and data scientists to understand data requirements and optimize data access and retrieval for AI models
Ensure data quality, integrity, and security across all data storage solutions
Support the deployment and maintenance of AI applications by providing expert data engineering capabilities
Familiarity with AI concepts in the context of data storage, access, and retrieval
Continuously optimize data infrastructure for performance, cost-efficiency, and scalability
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.
For mid-level data scientists, 3-6 years of experience on Data Science projects.
For senior-level data scientists, at least 6 years of experience on Data Science projects with at least 3 years of experience managing teams.
A TS/SCI with Polygraph security clearance.
For this particular role, you will specifically need:
8+ years of relevant experience. A higher degree may be accepted in lieu of years of experience.
Advanced proficiency in programming languages commonly used for data engineering (e.g., Python, Java, Scala)
Demonstrated expertise in designing, developing, and optimizing data pipelines for large-scale enterprise environments
Proven experience with corporate dataflows and developing data parsers for extremely large datasets
Extensive experience with various database technologies including graph databases (e.g., Neo4j), SQL databases (e.g., PostgreSQL, MySQL), NoSQL databases (e.g., MongoDB, Cassandra), and vector databases
Familiarity with cloud platforms (AWS, Microsoft Azure) for data storage and processing
These are important skills to have, but not necessarily mandatory:
Experience with data governance, data security, and compliance best practices
Familiarity with big data technologies such as Hadoop, Spark, or Kafka.
Experience with data warehousing concepts and tools
Continuous learning mindset to stay abreast of cutting-edge data engineering and AI advancements