REQUIRED QUALIFICATIONS:

• Demonstrated mastery of data exploitation and data enrichment, using tools such as ElasticSearch, Neo4j, and similar.
• Demonstrated experience with complex Extraction, Transformation, Load (ETL) pipelines with both NiFi and Pentaho and experience with batch data processing and data streamlining workflows.
• Demonstrated experience leveraging unsupervised machine learning models to answer complex customer queries.
• Demonstrated experience with a range of Python packages and graph databases.
• Demonstrated ability to write queries that span multiple big data repositories of differing levels of sensitivity.
• Demonstrated experience with backend software engineering processes and scientific computing with proven experience with relevant programming languages.

DESIRED QUALIFICATIONS

• Demonstrated track record of fulfilling Sponsor mission needs and scaling capabilities to production.
• Demonstrated experience leading complex data science teams.
• Demonstrated experience with machine learning solutions.
• Demonstrated experience working with foreign and domestic partners of the Sponsor.
• Demonstrated experience writing custom algorithms for data exploitation.
• Demonstrated experience with complex graph database queries.
• Demonstrated experience working in high operational tempo environments.
  What you can expect from us:
  BCore is proud to be an equal opportunity workplace. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, sexual orientation or any other characteristic protected by law

• Take the Sponsor’s mission needs and convert those into technical milestones consistent with the Sponsor’s data science requirements.
• Develop and manage data processing ETL pipelines.
• Provide ad hoc trainings to Sponsor’s data users on custom tools and data exploitation consist stent with established use cases.
• Utilize software such as Python, Neo4J, Streamlit, and similar to create user-facing tools that incorporate advanced data science methodologies and approaches.
• Identify ways in which those user-facing tools can leverage existing Large Language Models (LLMs) available to the Sponsor in order to accelerate data science workflows.
• Identify existing or emerging data science workflows within the Sponsor’s broader office that would accelerate the Sponsor’s data science mission.
• Leverage all available big data and data science coding platforms, tools, and similar to implement Sponsor’s technical goals for data management.
• Anticipate having to CONUS and OCONUS travel, infrequently at the Sponsor’s direction