Data Scientist

1. Data Scientist

Labor Category:  Data Scientist – Senior 

Clearance:  U.S. Citizen with active SECRET clearance (required).

Position Summary

Design, develop, and deploy advanced analytics and AI/ML models against commercial Science & Technology (S&T) data to enable data-driven S&T portfolio decisions for the ONR Comptroller's Data & Analytics Division. Works directly with Government expert analysts to answer strategic questions and turn data into insight.

A. Minimum Qualifications

• Ten (10) years developing and deploying analytical models for S&T or business requirements within a DoW/DoD organization.

• Ten (10) years in data science: statistical analysis, predictive modeling, machine learning, and algorithm development using Python, R, and SQL.

• Ten (10) years in data wrangling and building data pipelines (batch and streaming), ETL, and integrating structured and unstructured data from multiple sources.

• Experience on at least two (2) distinct projects applying advanced analytics (e.g., NLP, Network Analysis, Bibliometric Analysis) to S&T portfolio data.

• Ten (10) years using data visualization tools (Tableau, Power BI, matplotlib) to communicate findings to stakeholders.

• Five (5) years with big data technologies and cloud-based analytics platforms.

• Five (5) years applying operational analytics relevant to workforce, logistics, or readiness missions.

• Verifiable experience designing and deploying data solutions compliant with federal data security and governance frameworks (NIST SP 800-53, FISMA, DoD RMF).

B. On-Contract Responsibilities

• Identify, acquire, and curate relevant commercial S&T data sets (publications, grants, patents, startup investment activity, company details, informal S&T literature) and engage directly with ONR expert analysts. 

• Develop and deploy advanced analytics — predictive, prescriptive, descriptive, and cognitive/statistical/modeling methods — plus dashboards and AI/ML models in collaboration with ONR stakeholders. 

• Build ML/AI models including Natural Language Processing (NLP) to analyze unstructured text, using platforms such as AWS SageMaker or Azure Machine Learning. 

• Support a robust data quality program (profiling, cleansing, enrichment, stewardship) and automated quality checks at ingestion to flag anomalies, schema drift, or corrupted inputs. 

• Automate reports and configure dashboards providing real-time insight into global research trends; support onboarding and training toward a data-literate workforce. 

• Support Automated MLOps: monitor deployed models for data drift/model decay, trigger automated retraining when accuracy drops below baseline, and maintain a version-controlled Model Registry