Senior Data Scientist (Python, AI agents)

Senior Data Scientist

6 Months contract –   Extension

McLean , VA – On-site   M-F  

Interview Information:

Rounds: 2 rounds

Duration: 30-60 mins

Interview Type: MS Teams - Video Mandatory & In Person

 Must Have Qualifications:

•  Execution-focused Data Scientist with strong hands-on experience.
• Strong ability to follow and implement hands-on technical instructions.
• Deep understanding of Python.
• Strong experience with AI prompting and prompt engineering.
• Experience building and coding AI agents.
• Experience in designing, developing, and testing AI solutions.
• Experience working with AI tools and APIs and integrating them into applications.
• Familiarity with VS Code, Claude, coding agents, and version control systems.

Job Description:

• We are seeking a highly analytical and execution-focused Data Scientist to support the newly formed AI Execution function.
• This role will focus on turning raw data and emerging AI capabilities into practical, reusable solutions that improve efficiency and decision-making across the business.
• Unlike a traditional data science role centered on modeling alone, this position emphasizes problem understanding, rapid experimentation, data operationalization, and applied AI.
• The ideal candidate is someone who is intellectually curious, comfortable working with imperfect data, and skilled at navigating ambiguity to deliver impact.

Key Responsibilities

• Partner with business teams to understand complex problem statements and translate them into AI-enabled solutions
• Extract, clean, and structure data from raw and heterogeneous sources to enable analytics and GenAI use cases
• Develop Python-based prototypes and tools that leverage advanced analytics and generative AI
• Design and refine prompts to deliver reliable, explainable, and decision-ready AI outputs
• Collaborate with data, technology, and engineering partners to align prototypes with production standards
• Contribute reusable code, patterns, documentation, and demos to shared repositories to enable scaling and reuse
• Support testing, validation, and iteration of AI solutions prior to broader deployment What Success Looks Like
• Quickly grasps new problem domains and identifies practical paths forward
• Produces high-quality, well-reasoned prototypes that solve real business needs
• Elevates the quality and usability of data for multiple downstream use cases
• Acts as a multiplier by enabling others to reuse and build on their work