Data Scientist

CACI•Springfield, VA

8d•Onsite

About The Position

The Data Scientist will support the Business Analytics division which is responsible for identifying, acquiring, transforming, and analyzing the Agency's data to assess agency performance and inform senior leadership decision making. A qualified candidate will support key analytical tasks to include data integration and modeling; operational research and program evaluation; interpreting data made available by agency stakeholders and data owners; and reporting and presenting conclusions to leadership.

Requirements

Bachelor of Science degree or higher in statistics, mathematics, computer science, remote sensing, earth science, physical science or related technical or scientific field; or similar course of study at an accredited school (or substituted education and experience).
Minimum 7 years of experience.
Minimum 2 years of experience in developing in DBMS environments (Oracle, PostgreSQL, Microsoft SQL Server).
Minimum 2 years of experience in employing data ETL and manipulation activities (Pandas, NumPy, etc.).
Minimum 1 year of experience in deploying data mining capabilities in addition to relational data modeling efforts.
Minimum 1 year of experience using software packages used for advanced statistical analysis of operational data and tools for data visualization (Tableau, D3, ggplot, matplotlib).
Demonstrated participation in and/or directing the activities of highly skilled technical and analytical teams responsible for developing solutions to highly complex analytical/intelligence problems.
Ability to develop and deploy rigorous mathematical algorithms and applications in response to complex business use case requirements.
Ability to identify systems enhancements and new capabilities, concept demonstrators, capability business cases.
Ability to provide training as new systems and capabilities move into operational use.
Ability to work independently in a fast paced, matrix-oriented environment.

Nice To Haves

MS in a quantitative field such as Computer Science, Statistics, Applied Mathematics, etc.
At least 3 years of experience in deploying supervised and non-supervised machine learning algorithms within a Big Data environment.
At least 2 years of experience in planning and implementing data engineering activities encompassing structured, semi-structured, and non-structured data sets.
At least 2 year of experience working within an Agile oriented environment.
At least 1 year of experience working with AWS infrastructure.
Possesses working knowledge of version control platforms (GitLab, GitHub, etc.).
Exposure to marketing analytics, consumer analytics, operations analysis, human capital analytics, or other relevant fields.
Manipulate, synthesize and visualize large datasets from and between knowledge domains, using appropriate programming languages and software, including but not limited to Semantic Query Language (SQL), Apache Spark, Elasticsearch, R, Python, Tableau, Kibana and others as technology evolves.
Experience with providing customer service support activities across organizational elements.
Familiarity with Intelligence Community data, software, services and business analytic tools.
Exceptional oral and written communication and briefing skills.

Responsibilities

Identify, acquire, clean, transform, and analyze agency data to support performance assessment and leadership decision-making.
Integrate data from multiple sources and build data models to support analytical and operational needs.
Conduct operational research, statistical analysis, and program evaluations to understand trends and drive recommendations.
Interpret data provided by stakeholders and data owners to develop meaningful insights.
Develop, deploy, and maintain data mining solutions and relational data models.
Use statistical analysis and data visualization tools to analyze operational data and communicate results.
Create rigorous mathematical algorithms and data-driven applications to solve complex business problems.
Identify enhancements, new capabilities, proofs of concept, and business cases to improve systems and analytics.
Provide training and support as new data systems, tools, and capabilities transition to operational use.
Report and present analytical findings, insights, and recommendations to senior leadership in clear, actionable formats.
Support and guide technical and analytical teams working on complex data, intelligence, or analytics solutions.
Work independently in a fast-paced, matrixed environment while managing multiple analytical tasks.