PhD Data Scientist

• Experience building NLP, LLM, or using GenAI tools (e.g. LoRA, LangChain, RAG, LLM Fine Tuning or PEFT)
• Proficiency in Python (pandas, PySpark) and SQL
• Hands-on experience with data visualization tools like Power BI, Tableau, or Databricks
• Strong quantitative and analytic abilities to analyze and validate data
• Understanding of analytical model development and data-driven decision-making
• Must be a US Citizen

Bonus points if you have:

• Experience designing and implementing a custom Generative AI solution
• Utilized PhD training in rigorous research methodologies to analyze and tackle challenging client problems from a distinctive and informed perspective
• Experience working in a cloud environment (Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP))
• Experience working with federal clients
• Experience in a specific domain like Cybersecurity or Fraud Detection
• Active Clearance

Competitive pay with 401(k) match, strong health coverage, family-focused benefits, and paid time-off programs.

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! The Work: * Analyze & Innovate: Uncover trends and anomalies, turning raw data into strategic intelligence. * Model & Predict: Develop and maintain models using machine learning, AI, and statistical techniques. * Code & Transform: Use Python (pandas, PySpark) and SQL for data parsing, ETL, and complex analysis. * Visualize & Communicate: Create interactive dashboards and visualizations with Power BI, Tableau, or Databricks. * Collaborate & Build: Partner with Data Engineers to define data requirements and support development. * Explore & Experiment: Research and test new tools to solve challenging problems.