At Simple Technology Solutions, our people are our priority. We know our team members are more than employees—they’re parents, friends, volunteers, artists, and athletes. That’s why we offer flexibility to help them thrive personally and professionally while delivering exceptional solutions to our Federal Government clients.
Our culture is built on collaboration, continuous learning, and excellence. We are mentors and thought leaders who share knowledge and foster growth. Recognized as a “Best Place to Work,” we believe a range of perspectives helps us drive innovation and exceed customer expectations. At STS, taking care of our people isn’t a perk—it’s the standard.
As a HUBZone company, we also offer special incentives for team members living in qualified HUBZones. Check out the HUBZone map HERE to see if you qualify!
Simple Technology Solutions is looking for a Mid-Level Data Scientist to add to our team.
Quick Position Overview:
- US Citizenship is required
- Bachelor's Degree is required
- minimum of 3-5 years' position related experience is required
The Role:
STS is looking for a Mid-Level Data Scientist to join a federal data engineering team. You will work on a modern AWS-based federal data platform, building AI/ML capabilities and delivering production-ready analytical products that support critical government decision-making. A curiosity-driven mindset, strong quantitative skills, and the ability to translate analytical outputs into production-ready data products conforming to agency standards are prerequisites for this position.
This position is contingent upon contract award.
The Mid-Level Data Scientist at STS will:
- Build and maintain knowledge bases, vector stores, and Retrieval Augmented Generation (RAG) pipelines using Amazon Bedrock and Amazon OpenSearch Services to make financial and regulatory datasets AI-ready for advanced analytics and machine learning consumption
- Support the development, validation, and operationalization of statistical outputs and derived data products; coordinate with the agency data science team and SME data scientists to implement Airflow DAGs and AWS Glue jobs that ensure automated, recurring updates
- Support transition of data science outputs into production by validating accuracy, completeness, and reporting readiness; ensure all production data products are incorporated into the agency's ETL load and gap reporting infrastructure
- Develop and validate machine learning models and analytical pipelines using large-scale financial and regulatory datasets in the data lake
- Leverage AI-assisted development tools for code generation, debugging, and performance tuning; adhere to agency security standards and applicable federal AI governance requirements
- Write Python 3.10 code conforming to PEP 8; integrate analytical pipelines with the agency's ETL metadata infrastructure and produce required load and gap reporting outputs
- Support entity resolution work to ensure consistent identification and linkage of records across high-volume financial datasets
- Produce required documentation for all analytical models and pipelines: methodology, data lineage, model assumptions, refresh schedules, and IV&V Questionnaires
- Write automated tests achieving the 90% minimum code coverage threshold; complete security scans at least once per sprint as part of the Definition of Done per OWASP ASVS Level 2
- Participate in 2-week sprint ceremonies, quarterly PI planning, backlog refinement, and agile delivery using JIRA and GitHub
Education and Experience:
Required
- Bachelor's degree or higher in Data Science, Statistics, Computer Science, Mathematics, or a related quantitative field
- 3-5 years of experience in data science, machine learning engineering, or quantitative analytics
- Proficiency in Python 3.10 (PEP 8) including pandas, NumPy, scikit-learn, and related libraries
- Hands-on experience with Amazon Bedrock, knowledge bases, vector stores, and RAG pipeline design on AWS
- Experience with Amazon OpenSearch Services or equivalent vector/search infrastructure
- Experience with Apache Airflow (MWAA) for DAG-based pipeline orchestration
- Familiarity with AWS Glue, S3, and Apache Spark for large-scale data processing
- Experience with SQL and query tools such as Trino, Athena, or Redshift
- Experience working with large-scale financial or regulatory datasets is strongly preferred
- Knowledge of federal AI governance requirements and responsible AI practices in a government setting
- Experience with agile development, CI/CD pipelines, GitHub, and sprint-based delivery
- Familiarity with FISMA, NIST 800-53, and Zero Trust principles
- Must be able to work 8am-5pm Eastern Time regardless of home location
- Active federal public trust suitability determination or ability to obtain one required
STS is committed to equal employment opportunity and merit-based employment practices. STS provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination, harassment, and retaliation in all employment practices and decisions in accordance with applicable federal, state, and local laws.
Employment decisions at STS are based on individual qualifications, performance, skills, and business needs, without regard to race, color, religion, sex, national origin, age, disability, protected veteran status, sexual orientation, gender identity, genetic information, marital status, or any other status protected by applicable law.
This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, compensation, training, transfer, discipline, termination, layoff, recall, and leaves of absence.
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