AI/ML Ops Data Scientist – Jr.

Job Family:

Data Science & Analysis

Travel Required:

Up to 10%

Clearance Required:

Active Secret

What You Will Do:

• Provide data science, artificial intelligence (AI) / machine learning (ML), and analytics support to Department of State clients.

• Identify and address client needs, build client relationships, and drive initiatives forward.

• Focus on a single priority project or drive multiple concurrent projects forward.

• Collaborate with other data scientists and adjacent roles.

• Wrangle, cleanse, and analyze data using Python.

• Develop, evaluate, monitor, and maintain AI/ML predictive models and Natural Language Processing models using Python.

• Develop advanced data visualizations and dashboards using Power BI or Tableau using best practices.

• Perform data science in an Azure environment.

• Leverage data science platforms such as Databricks.

• Work with Lange Language Models.

• Containerize ML models using tools such as Docker and help manage deployments on OpenShift.

• Write technical process flows, diagrams, and model documentation.

• Present products and findings to clients and answer questions about methods and results.

• Stay abreast of the latest advancements in AI/ML methods and technologies and apply them as appropriate.

• Ensure AI/ML deployments adhere to commercial and public sector guidelines, policies and standards, delivering responsible use of AI.

• Develop trusted relationships with clients at all levels of the organization to obtain a more complete perspective and understanding of our clients’ mission, challenges, and goals to deliver tailored solutions and drive innovation in AI/ML and data science.

• Support business development efforts (e.g., responding to RFPs/RFIs, developing white papers, creating pitch decks and capability briefings, etc.).

• Support internal firm initiatives.

• Continue to develop professionally and expand skills related to data science and consulting.

What You Will Need:

• Active SECRET (or higher) security clearance.

• Bachelor’s degree in a relevant technical field.

• Solid understanding of various supervised and unsupervised ML methods and techniques.

• Proficiency in wrangling data and developing AI/ML models using Python.

• Experience utilizing Large Language Models.

• Understanding of Natural Language Processing.

• Understanding of ML model containerization, such as with Docker.

• Understanding of DevSecOps principles.

• Ability to learn new technical skills.

• Ability to operate independently or collaboratively in small teams.

• Strong communication and presentation skills for both technical and non-technical audiences.

• Ability to understand client mission, business processes, and data nuances and adapt data science solutions accordingly.

• Ability to think strategically and drive innovation.

• Ability to operate successfully on remote, hybrid, or on-site projects in the DC metro area.

What Would Be Nice To Have:

• Master's degree in a relevant technical field.

• ONE (1) or more years of relevant professional experience in AI/ML and data science.

• Experience developing data science solutions on cloud platforms (e.g., AWS, Azure, GCP).

• Experience utilizing the Databricks platform to conduct data science.

• Proficiency in developing data visualizations and dashboards using Power BI or Tableau, applying best practices.

• Proficiency utilizing SQL to query and wrangle data.

• Experience supporting DevSecOps, including integrating security into CI/CD workflows for AI/ML models.

• Experience with Docker for containerizing ML models and managing container lifecycles, including building Docker images.

• Experience with Jenkins for building and automating CI/CD pipelines.

• Experience utilizing GitHub for version control, branching strategies, and CI/CD pipeline integration.

• Experience in OpenShift for deploying and managing containerized applications in a Kubernetes-based environment.

• Experience with deploying AI/ML models on cloud platforms (e.g., AWS, Azure, GCP) and hybrid cloud deployments, including cloud security (e.g., Managed Identities).

• Knowledge of Infrastructure as Code.

• Knowledge o