Job description

ACTIVE SECURITY CLEARANCE AT THE TS/SCI POLYGRAPH LEVEL IS REQUIRED We are seeking a versatile Data Scientist / Machine Learning Engineer to join our team in Annapolis Junction, MD, leveraging artificial intelligence and advanced analytics to transform massive datasets into strategic mission insights. In this role, you will take full ownership of the AI/ML lifecycle—from designing mathematical models and writing automated Python workflows to deploying predictive models into cloud-native processing platforms. You won't just build models in isolation; you will manipulate large-scale datasets using PySpark and SQL, build compelling data visualizations, and work side-by-side with cross-functional teams to tackle high-stakes data challenges. If you thrive on blending computational mathematics, predictive modeling, and scalable machine learning to uncover hidden trends in large datasets, you’ll fit right in with Team Nyla. The annual base salary range for this role is $152,000-$180,000 (USD) , which does not include discretionary bonus compensation or our comprehensive benefits package. Actual compensation offered to the successful candidate may vary from posted hiring range based upon geographic location, work experience, education, and/or skill level, among other things. • AI/ML Model Development & Deployment: Hands-on experience developing, training, validating, and deploying machine learning models in production environments. • Large-Scale Data Processing: Practical background utilizing Apache Spark or cloud data platforms for distributed, large-scale dataset manipulation. • Programming & Querying Proficiency: Strong expertise in Python (including data science packages) and SQL for data modeling, extraction, and transformation. • Statistical & Predictive Modeling: Background in advanced statistics, computational mathematics, and quantitative/qualitative analytical techniques. • Data Visualization & Analytics Automation: Demonstrated skill creating automated analytics workflows and building intuitive dashboards/visualizations using Tableau , Power BI , or Python libraries. Education: Bachelor’s Degree in Data Science, Computer Science, Applied Mathematics, Statistics, or a related quantitative discipline, PLUS 7+ years of professional experience in data science, predictive modeling, and AI/ML engineering OR Master’s Degree in a quantitative discipline, PLUS 5+ years of specialized experience developing and deploying machine learning models on large datasets OR High School Diploma / GED , PLUS 11+ years of hands-on technical experience in data science, advanced analytics, and predictive modeling in lieu of a degree. Target Knowledge / OCP Experience: Target knowledge or direct experience with OCP (Operational Capability Packages / Operational Target Frameworks). Cloud Data Architectures: Experience leveraging cloud-native analytics services (e.g., AWS SageMaker, EMR, Databricks) for distributed model training and data storage. Cross-Functional Collaboration: Strong history of collaborative problem-solving within multi-disciplinary engineering and intelligence teams.

Requirements

Must-haves

ACTIVE SECURITY CLEARANCE AT THE TS/SCI POLYGRAPH LEVEL IS REQUIRED
AI/ML Model Development & Deployment: Hands-on experience developing, training, validating, and deploying machine learning models in production environments.
Large-Scale Data Processing: Practical background utilizing Apache Spark or cloud data platforms for distributed, large-scale dataset manipulation.
Programming & Querying Proficiency: Strong expertise in Python (including data science packages) and SQL for data modeling, extraction, and transformation.
Statistical & Predictive Modeling: Background in advanced statistics, computational mathematics, and quantitative/qualitative analytical techniques.
Data Visualization & Analytics Automation: Demonstrated skill creating automated analytics workflows and building intuitive dashboards/visualizations using Tableau , Power BI , or Python libraries.
Target Knowledge / OCP Experience: Target knowledge or direct experience with OCP (Operational Capability Packages / Operational Target Frameworks).
Cloud Data Architectures: Experience leveraging cloud-native analytics services (e.g., AWS SageMaker, EMR, Databricks) for distributed model training and data storage.
Cross-Functional Collaboration: Strong history of collaborative problem-solving within multi-disciplinary engineering and intelligence teams.

Nice-to-haves

None flagged.

Application prep

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Keywords to mirror

machine learningaimlpythonsqlaws

Platform: agile-ats
Effort: medium