Full Stack Python Engineer
Accenture Federal Services - Washington, DC
Posted May 12, 2026
Benefits
- Parental leave
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- Non-birth-parent leave
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- Family-building benefits
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- Fertility benefits: Not verified
- Adoption assistance: Not verified
- Surrogacy assistance: Not verified
- Mental health support
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- Relocation assistance
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- Childcare support
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- Learning budget
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- Salary
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- 401(k) match
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Schedule
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- Weekend work
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Application
- Cover letter
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- Assessment
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- Deadline
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Where they hire
State eligibility is not yet verified.
About this role
Full Stack Python Engineer Washington, DC 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: Key Responsibilities: Technical Leadership & Code Ownership - Set technical direction for an 8-10 person engineering team. - Serve as primary architect and code-quality gatekeeper (review/merge all GitHub PRs). - Define and enforce standards for architecture, performance, testing, documentation, and CI/CD. - Own the end-to-end codebase for a distributed forecasting platform built on Python and Apache Spark (GCP). - Champion and evolve the internal ML tooling library used for training, evaluation, metrics, and visualization. Forecasting Platform Development - Build and maintain scalable data pipelines and feature engineering workflows for intermittent and lumpy MRO/spare‑parts demand. - Extend and improve metrics instrumentation (sMAPE, MASE, Dollar‑Weighted MAE, bias, diagnostics). - Support multi-model training, evaluation, selection, calibration, and DFU lifecycle management. - Integrate and maintain MLflow for experiment
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