Journeyman Data Scientist (two positions)

IDS is building a candidate pipeline for data scientists supporting a federal applied research program in acquisition cost analytics. The lead journeyman position builds and maintains the automated data pipelines, feature engineering, model tuning, and visualizations that the research depends on.

The customer designates this position as key personnel, so qualification thresholds and clearance status are verified before any candidate is proposed. More than one seat is expected.

• Minimum five years of data science experience.
• Proficiency in Python and R, including data manipulation libraries and machine learning frameworks, for exploratory analysis and model building.
• Competence developing and maintaining data pipelines, with knowledge of extraction, transformation, and loading processes and frameworks.
• Advanced knowledge of Databricks or an equivalent platform for efficient data collection, transformation, and loading.
• Skill presenting findings using visualization tools such as Tableau or Power BI.
• Experience working with diverse teams and translating data driven insight into actionable recommendations.
• Active Secret clearance. United States citizenship required.

• Experience with Apache Spark and cloud based data warehousing.
• Exposure to federal acquisition, financial, or program management data.
• Experience working inside a government authorized cloud environment.

This position is expected to pay $125,000 to $155,000 annually; depending on experience, education, and any certifications that are directly related to the position

IDS is committed to providing superior work in the fields of science, engineering, data analytics and technology to government agencies. We offer competitive compensation packages, including comprehensive nationwide Medical/Dental/Vision insurance programs, life insurance, matching 401k contribution and Educational/Training support.

• →Develop and maintain automated data pipelines, including extraction, transformation, and loading processes over large and heterogeneous source data.
• →Implement automated data quality checks, validation rules, error handling, and logging.
• →Build and maintain metadata tagging and data lineage so that any analytical result can be traced to the source record behind it.
• →Maintain all pipeline code, configuration, and infrastructure-as-code in a government version control repository, with documentation sufficient for independent redeployment.
• →Improve existing models through hyperparameter tuning, cross validation, and feature engineering.
• →Build dashboards and visualizations that communicate analytical findings clearly.
• →Work across a mixed team of cost analysts and engineers, translating analytical results into recommendations.