Data Scientist

Agile Defense•Omaha, NE

57d

About The Position

At Agile Defense we know that action defines the outcome and new challenges require new solutions. That’s why we always look to the future and embrace change with an unmovable spirit and the courage to build for what comes next. Our vision is to bring adaptive innovation to support our nation's most important missions through the seamless integration of advanced technologies, elite minds, and unparalleled agility—leveraging a foundation of speed, flexibility, and ingenuity to strengthen and protect our nation’s vital interests. As a Data Scientist at Agile Defense, you will be joining a team of professionals that build AI/ML solutions. This role will help support understanding our customers, markets, and operations through advanced analytics and machine learning. You'll work closely with business leaders, product teams, and engineering to identify opportunities, test hypotheses, and build predictive models that inform critical business decisions and drive measurable impact.

Requirements

3+ years of experience regression analysis, and building predictive models for business applications with measurable impact
Advanced statistical knowledge including experimental design, hypothesis testing, causal inference, and statistical modeling techniques
Strong programming skills in Python or R with experience in data analysis libraries and platforms like Databricks for large-scale analytics
Business acumen with proven ability to translate complex analytical findings into strategic business recommendations and actionable insights
Machine learning expertise with experience building and validating models using scikit-learn, TensorFlow, or similar frameworks on platforms like Databricks
Data analysis and visualization abilities using advanced analytics tools and integrating with business intelligence platforms (Tableau, PowerBI, Qlik)
Database and analytical platform experience with SQL, statistical software, and modern data analysis environments

Nice To Haves

Production ML systems experience including model deployment, monitoring, and MLOps practices in cloud environments
Strong data engineering skills in Databricks, Python, PySpark, and Spark SQL with experience implementing medallion architecture and modern data platform patterns
Data architecture expertise with experience designing scalable data processing systems and implementing data governance frameworks
Experience integrating with platforms such as Qlik, Tableau, PowerBI, MAVEN Smart System (Palantir), or similar.
Experience designing and building enterprise-level dashboards, reports, and self-service analytics platforms
Analytics platform knowledge including experience with Advana

Responsibilities

Drive Data-Driven Discovery
Conduct deep-dive analyses to understand customer behavior, market trends, and business performance
Design and execute sophisticated experiments to test business hypotheses and measure feature impact
Build predictive models that forecast business outcomes and identify growth opportunities
Apply advanced statistical techniques to solve complex business problems across multiple domains
Lead Analytics Strategy
Partner with business leaders to identify high-impact analytical opportunities and frame research questions
Influence product and business strategy through data-driven insights and recommendations.
Build analytical frameworks.
Present complex findings to executive stakeholders in compelling, actionable formats
Build Innovative ML Solutions
Develop models for segmentation, recommendation systems, risk assessment, and optimization
Create statistical models that quantify business impact and guide resource allocation decisions
Collaborate with ML engineers to transition research models into production systems
Enable Advanced Analytics
Establish statistical rigor and experimental design standards across the organization
Build analytical tools and methodologies that other teams can leverage for their own insights
Foster Data-Driven Culture
Train and mentor stakeholders on analytical thinking and data interpretation
Communicate complex statistical concepts to non-technical audiences in understandable ways
Advocate for data quality and proper experimental design in business decision-making
Contribute to the analytical capabilities and methodologies of the broader data team