Proficient in Python, R, or similar data analysis languages
Strong foundation in statistics and machine learning
Experience with large, complex datasets
Ability to communicate technical findings to diverse audiences
Collaborative mindset with independent work capability
Degree in Data Science, Computer Science, Statistics, Mathematics, or equivalent experience
Responsibilities
Develop machine learning and statistical models to extract insights from complex datasets
Explore and select models based on performance and scalability
Build experiments and generate synthetic data when real data is limited
Create visualizations and reports to communicate findings
Automate manual analyses into repeatable analytic solutions
Engineer features and prepare data for machine learning workflows
Tune models to optimize performance across varying datasets
Evaluate model effectiveness using standard techniques
Collaborate with software engineers to deploy analytics in production
Contribute to scalable solutions for large, evolving datasets
Benefits
Collaborative work environment
Opportunities for professional development
Access to cutting-edge technology
Engagement in impactful projects
Support for work-life balance
Full Job Description
Your Impact
You'll work across the full data science lifecycle-exploration, modeling, validation, and operationalization-on problems that demand both technical depth and creative thinking. Day-to-day, that can look like:
Developing machine learning, statistical, data mining, and graph-based models to extract insight from complex datasets
Exploring multiple approaches and selecting models based on performance, scalability, and mission need
Building experiments and generating synthetic data when real-world data is limited or incomplete
Creating visualizations and reports that clearly communicate findings and drive decision-making
Translating manual or ad hoc analysis into automated, repeatable analytic solutions
Engineering features and preparing data for use in machine learning workflows
Tuning models and parameters to optimize performance across varying datasets
Evaluating model effectiveness using standard techniques (e.g., cross-validation, ROC curves, confusion matrices)
Partnering with software engineers and cloud teams to deploy analytics into production environments
Contributing to scalable solutions that can handle large, evolving datasets
Supporting or leading analytic efforts, depending on your experience level
Qualifications
All positions require an active TS/SCI with polygraph
Experience with Python, R, or similar languages used for data analysis and modeling
Strong foundation in statistics, machine learning, or data analysis techniques
Experience working with large, complex, or imperfect datasets
Ability to communicate technical findings clearly to both technical and non-technical audiences
A collaborative mindset with the ability to work independently and as part of a team
A degree in Data Science, Computer Science, Statistics, Mathematics, or a related field (or equivalent experience)
Nice to Have
Experience with cloud environments (AWS), big data tools, or distributed processing frameworks
Familiarity with ML pipelines, model deployment, or MLOps concepts
Exposure to graph analytics, NLP, or AI-driven systems
Experience working in mission or high-performance computing environments
