Data Scientist

About the role

About the Role:   We are looking for a highly capable Data Scientist to join our Enterprise AI & Analytics organization. Reporting directly to the Head of AI & Analytics, you will take ownership of individual data science projects from start to finish, while also serving as a key technical contributor to our company-wide AI modernization efforts. In this role, you will independently manage analytics deliverables, build predictive and generative AI prototypes, and help turn high-level AI strategy into deployed, practical solutions for the business. What You’ll Do: Independent Project Execution: Scope, manage, and deliver data science and analytics projects with a high degree of autonomy. Handle everything from data wrangling and exploratory analysis to model building and interactive dashboarding (e.g., Streamlit, Power BI). AI Modernization Support: Assist leadership in executing the corporate AI roadmap. Evaluate new AI tools, test agentic workflows, and help integrate LLM capabilities into existing enterprise processes. Technical Prototyping: Build and deploy applied AI and machine learning solutions using modern cloud infrastructure and APIs to automate workflows and enhance business intelligence. Promote Responsible AI: Operationalize data governance and AI safety standards set by leadership (such as aligning with the NIST AI Risk Management Framework), ensuring all technical deliverables are secure, reproducible, and well-documented. Cross-Functional Delivery: Track project timelines, communicate technical roadblocks, and present analytical findings clearly to both engineering peers and non-technical stakeholders.   What We’re Looking For (Must-Haves): Experience: 3–6 years of hands-on experience in data science, analytics, or applied machine learning, with a proven ability to manage project lifecycles independently. Education: Bachelor’s or Master’s degree in Mathematics, Statistics, Data Science, Computer Science, or a related quantitative field. Technical Fluency: Strong proficiency in Python and SQL. Experience with standard data science libraries, version control (Git), and data visualization. Execution Focus: A builder’s mindset. You are comfortable taking a strategic objective from leadership and figuring out the technical steps required to make it a reality. Communication: Excellent ability to document methodologies and present complex data clearly to business audiences.   Nice to Have: Cloud Ecosystem: Hands-on experience building pipelines or deploying models in Microsoft Azure (e.g., Azure SQL, Azure DevOps, Azure AI Foundry, Function Apps). Domain Expertise: Background in federal contracting, defense consulting, or enterprise workload modeling. An existing security clearance is a strong plus. Modern Tooling: Experience with LLM integrations, prompt engineering, or developing user interfaces for data applications.