Data and AI Analyst

Accenture Federal Services
Arlington, VAPosted 9 May 2026

Job Description

At Accenture Federal Services, nothing matters more than helping the US federal government make the nation stronger and safer and life better for people. Our 13,000+ people are united in a shared purpose to pursue the limitless potential of technology and ingenuity for clients across defense, national security, public safety, civilian, and military health organizations. Join Accenture Federal Services, a technology company within global Accenture. Recognized as a Glassdoor Top 100 Best Place to Work, we offer a collaborative and caring community where you feel like you belong and are empowered to grow, learn and thrive through hands-on experience, certifications, industry training and more. Join us to drive positive, lasting change that moves missions and the government forward! Data and AI Analyst Start Your Career Where Innovation Meets Purpose At Accenture Federal Services, we help the U.S. federal government make the nation stronger, safer, and better for all. Our 13,000+ team members are united by a shared purpose: to harness the limitless potential of technology and human ingenuity for clients across defense, national security, public safety, civilian, and military health organizations. Join a collaborative and caring community where you belong, and where your growth is our priority. As an entry-level analyst, you’ll gain hands-on experience, earn industry certifications, and receive structured training that builds on your engineering mindset—applying creativity and analytical thinking to solve real-world challenges. We’re writing the playbook for how to be the most AI-enabled, client-focused, and people-first organization. Our reinventors are driving faster value for clients and shaping the future of government services. Are you ready to reinvent what’s possible? It’s your career, your life, and your choice. Let’s build something that matters—together. The Opportunity At AFS, our Data AI Analysts help federal agencies unlock the power of data and artificial intelligence to drive smarter decisions, faster operations, and better outcomes for the nation. You’ll begin your journey with immersive training in data engineering, analytics, and AI integration—building the skills to solve complex challenges across defense, national security, and civilian sectors. In this role, you’ll apply your engineering mindset to design and implement data-driven solutions using modern tools, cloud platforms, and AI frameworks. You’ll work side-by-side with teammates to experiment with emerging technologies, adopt next-generation IT, and deliver mission-critical insights that move government forward. What You’ll Do: Design, build, and maintain data pipelines and analytics platforms using tools like Python, SQL, Spark, and cloud-native services. Apply machine learning and AI techniques to extract insights, automate processes, and support predictive decision-making. Leverage frameworks such as Agentic AI to develop intelligent systems that adapt and respond to dynamic mission needs. Visualize data and communicate findings using tools like Tableau, Power BI, and custom dashboards. Collaborate with cross-functional teams to integrate AI models into operational workflows and ensure responsible, secure use of data. Experiment with emerging technologies and next-gen architectures to accelerate solution delivery and scale impact. What You’ll Learn AI engineering and model deployment using platforms like TensorFlow and PyTorch. Data architecture, cloud analytics, and scalable data processing on AWS, Azure, or GCP. Responsible AI practices, data governance, and ethical use of machine learning in federal environments. Agile delivery, DevSecOps, and continuous learning through certifications and mentorship. Basic Qualifications Bachelor’s or Master’s degree in a STEM or adjacent field (e.g., Computer Science, Statistics, Mathematics, Data Science), with graduation by August 31, 2026. Engineering background with foundational skil ... (truncated, view full listing at source)
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