Mid-Level Data Scientist

Role Description

Simple Technology Solutions 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.

• 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.

Qualifications

• 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.

Benefits

• Flexibility to help team members thrive personally and professionally.
• Special incentives for team members living in qualified HUBZones.