*** Please note that this position is contingent upon the successful award of a contract currently under bid.***

The Mid-Level Data Scientist applies advanced machine learning, statistical modeling, and data science techniques to automate intelligence workflows, develop predictive analytic models, and ensure AI system quality across DIA's OBI production ecosystem. All technical solutions must integrate into DIA's existing software baseline (Python, R, SQL, ArcGIS, Tableau).

Duties may include:

• Develop, train, validate, and deploy ML and AI models for entity extraction, pattern detection, anomaly prediction, and multi-INT data fusion within classified DIA environments.

• Design and execute TEVV protocols for all AI/ML models, ensuring algorithms are unbiased, secure, and compliant with ICD 203 Analytic Standards and DoD Ethical Principles for AI.

• Build and deploy forward-looking predictive analytic models and NLP capabilities to increase timeliness, accuracy, and visualization quality of intelligence assessments.

• Build and optimize ETL pipelines using Python, SQL, Databricks, and Apache NiFi Data Engineering & Architecture requirements to ingest and normalize structured and unstructured intelligence datasets.

• Design and maintain statistical data quality monitoring frameworks using SHACL constraint validation and automated anomaly detection to proactively identify and remediate quality risks.

• Produce analytic visualizations using Kibana, Tableau, Power BI, or Python Complex Network Visualization requirement to map complex threat networks and illuminate key nodes for analysts.

• Ensure AI/ML model outputs align with DIEKM/DICO semantic standards and can be ingested into the MARS knowledge graph Ontology & Knowledge Modeling requirements.

• Document model architectures, training data lineage, bias assessment results, and performance benchmarks AI-Specific Requirements and NIST AI RMF 1.0.

• Participate in all SAFe ceremonies; deliver demo-ready model outputs at each Program Increment; contribute data science user stories to Product Owner backlogs.

Education and Experience

Required:

• Bachelor's degree in Computer Science, Data Science, Mathematics, Statistics, or related STEM field AND 3-6 years of data science, ML, or applied AI experience in classified IC or DoD environments

• Proficiency in Python (scikit-learn, TensorFlow, PyTorch) and SQL; experience with DIA baseline tools including R, ArcGIS, Tableau

• Experience with Apache NiFi, Databricks, Kafka, or equivalent enterprise ETL platforms for data pipeline development

• Demonstrated experience building and deploying ML models in production classified environments with documented TEVV processes

• Familiarity with NLP techniques: named entity recognition, sentiment analysis, topic modeling, or text classification

• Understanding of AI model documentation requirements: training data lineage, provenance tracking, bias assessment per NIST AI RMF 1.0

Desired:

• Experience with DIA MARS platform, TALOS program, or Object-Based Production (OBP) frameworks

• Familiarity with RDF/SPARQL, SHACL, OWL, or semantic web standards for ML-to-graph integration

• Experience with Elastic Search, Kibana, or Splunk for analytics, dashboarding, and quality monitoring

• Knowledge of NIST AI RMF, ICD 203, or DoD AI Ethical Principles

• SAFe Agile certification or Agile ML development environment experience

• Experience with RAG (Retrieval-Augmented Generation) or LLM integration in classified environments

• Master's degree or PhD in a relevant quantitative field

Security Clearance:

• Active TS/SCI and the willingness to sit for a polygraph, if needed

IC-CAP provides equal employment opportunities (EEO) to all applicants for employment without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, disability, genetic information, marital status.