Senior Data Scientist

• Distributed Data Processing and Big Data Architectures (e.g., Spark, Hadoop, Ray, Dask, Kafka)
• Data Pipeline/ETL Development and Orchestration (e.g., Airflow, Dagster, NiFi)
• Batch and Streaming Data Processing
• Data Modeling, Schema Design, and Data Quality Assessment
• Data Governance, Access Controls, and Secure Data Handling
• Cloud-based Data Platforms and Storage Architectures

Software Development and MLOps

• Computer Programming and Databases - Proficiency in Python and SQL required
• Software Engineering Best Practices (e.g., version control, testing, modular design)
• MLOps and Model Operationalization (e.g., model deployment, monitoring, reproducibility, containerization)
• Linux-based Development Environments, Git, Containers, and Cloud Platforms

Expectations for this position are as follows

• Rapidly prototypes and evaluates Data Science, Data Engineering, and AI/ML approaches to assess feasibility and support project needs.
• Leads simple projects or has accountability for tasks on a complex project; plans and guides the work of others.
• When in a contributor role, works independently and requires minimal instruction, guidance, and direction.
• Identifies and solves complex problems; takes new perspectives on existing solutions; exercises judgment within the Data Science and Data Engineering disciplines.
• Leverages advanced knowledge of Data Science and Data Engineering and shares that knowledge with others.
• Communicates complex results, both in writing and verbally, to technical and non-technical audiences.

The successful candidate for this position is a hybrid Data Scientist and Data Engineer who combines strong foundations in computer science, mathematics, and software engineering with hands-on experience designing data systems and applying advanced analytics, machine learning, and AI techniques to solve mission problems. This role requires practitioners who can operate across the full data lifecycle from data modeling, processing, and infrastructure engineering to statistical analysis, machine learning, experimentation, and operational deployment. Our organization values innovation and believes that keeping up with the latest research and technologies is essential.

The candidate should have hands-on experience in several of the areas specified below, however it is not required that a candidate has experience in all of these areas.

Advanced Data Analytics and Artificial Intelligence

• Machine Learning and Model Evaluation (e.g., supervised, unsupervised, deep learning, reinforcement learning)
• Statistical Analysis and Exploratory Data Analysis (e.g., descriptive statistics, regression, visualization)
• Large Language Models and AI-Assisted Workflows
• Natural Language Processing (e.g., summarization, classification, entity recognition, topic modeling)
• Anomaly Detection and Predictive Modeling
• Graph Analytics (e.g. centrality, similarity, link prediction), * Bachelor's degree in Computer Science, Applied Mathematics, Statistics, Data Science, or related field with 5 years of relevant experience.
• Hands-on proficiency with advanced Data Science techniques, including Machine Learning (e.g., supervised, unsupervised, deep learning, reinforcement learning), anomaly detection, statistical analysis, and/or analysis of very large datasets.
• Computer programming skills (e.g., Python, R, C/C++, Java, Scala, Julia, Rust, MATLAB).
• The ability to obtain and maintain a DoD Top Secret clearance within 12 months of hire.
• Per the U.S. Government's eligibility requirements, you must be a U.S Citizen to be considered for a security clearance.
• This position requires a minimum of 50% hybrid on-site, * Master's degree in Computer Science, Applied Mathematics, Statistics, Data Science, or related field with 3 years of relevant experience; PhD in Computer Science, Applied Mathematics, Statistics, Data Science, or related field.
• Active DoD Top Secret Clearance.

This requisition requires the candidate to have a minimum of the following clearance(s)

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