Data Scientist

ORBIS Inc.; Washington (District of Columbia); On-site; USD 120,000 - 180,000; Full time

Job summary

ORBIS Inc. in Washington, DC is seeking a Data Scientist to own end-to-end analytics projects within the Enterprise AI & Analytics organization.

You will build predictive and generative AI prototypes, turn high-level AI strategy into deployed solutions, and collaborate with engineering and business teams to drive measurable impact.

Strong Python/SQL, data visualization, and a builder mindset are essential, with experience in Azure tools and cloud-based workflows a plus.

Qualifications

• 3–6 years of hands-on data science, analytics, or applied ML with independent project lifecycles.
• Bachelor's or Master's in Mathematics, Statistics, Data Science, Computer Science, or related field.
• Strong proficiency in Python and SQL; experience with data viz, Git, and standard DS libraries.
• Execution-focused with ability to translate strategic objectives into technical steps.
• Excellent documentation and ability to present findings to both technical and non-technical audiences.

Responsibilities

• Scope, manage, and deliver data science projects end-to-end with autonomy.
• Build predictive and generative AI prototypes and dashboards (Streamlit, Power BI).
• Support AI modernization by evaluating tools and integrating LLM capabilities.
• Promote Responsible AI with governance and security standards.
• Communicate roadblocks and findings to stakeholders across teams.

Skills

Python

SQL

Data Visualization

Git

Streamlit

Power BI

Communication

Execution Mindset

Documentation

Analytical Thinking

Education

Bachelor's or Master's in Mathematics, Statistics, Data Science, CS or related quantitative field

Tools

Azure SQL

Azure DevOps

Azure AI Foundry

Power BI

Job description

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.