Accenture Federal Services
AI / ML Engineer
About this role
Accenture Federal Services seeks an AI/ML Engineer to develop data-driven solutions for U.S. federal government clients in defense and national security. The role involves building machine learning models, conducting statistical analysis, and preparing data for predictive analytics using Python, R, and modern ML frameworks.
What you'll do
- Develop and implement data-driven solutions using Python, R, and similar programming languages
- Conduct statistical analyses including regression, hypothesis testing, and distribution analysis
- Design and train machine learning models using TensorFlow, SageMaker, and Scikit-Learn
- Perform data wrangling to clean and prepare raw datasets for analysis
- Collaborate with cross-functional teams to understand business requirements and deliver insights
- Create data visualizations and reports for stakeholder communication
What they're looking for
- Python or R programming
- Machine learning frameworks (TensorFlow, Scikit-Learn, SageMaker)
- Statistical analysis and hypothesis testing
- Data wrangling and preparation
- NumPy, pandas, and visualization libraries
- Problem-solving and analytical thinking
- C++ or Go Lang experience
- Cross-functional collaboration
Benefits
- Collaborative and caring work environment
- Professional development through hands-on experience and certifications
- Industry training opportunities
- Recognized as Glassdoor Top 100 Best Place to Work
- Comprehensive benefits package
- Growth and learning opportunities
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Accenture Federal Services
Accenture Federal Services builds and maintains mission-critical technology solutions for the U.S. federal government, including cloud infrastructure, enterprise systems integrations, and cyber defense tools. The company is hiring DevOps engineers, full-stack developers, SAP specialists, and test engineers to support classified and unclassified federal government projects.
- Website
- accenturefederal.com
Likely interview questions
- Walk us through a machine learning project where you used TensorFlow or Scikit-Learn. What was the problem, and how did you evaluate your model's performance?
- Describe your experience with data wrangling and cleaning. What techniques do you use to handle missing values, outliers, and inconsistent data in large datasets?