> Markdown version of [/jobs/ext/4303345-data-scientist-sme](https://www.wearedevelopers.com/jobs/ext/4303345-data-scientist-sme). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Data Scientist - SME

- **Company:** Accenture
- **Location:** Chantilly, VA, United States
- **Experience:** Expert
- **Salary:** $109,500.0 - $213,500.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Business Analytics Applications, Data Analysis, Big Data, Data Governance, R (Programming Language), Statistical Hypothesis Testing, Python (Programming Language), Machine Learning, Tensorflow, SQL Databases, Feature Engineering, Pytorch, Large Language Models, Prompt Engineering, Model Validation, Generative AI, Scikit Learn, Information Technology, Data Analytics
- **Published:** October 20, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=deb0ea98e347439e

## About the Role

The ideal candidate is a technical leader with expertise in data science methodologies, predictive analytics, data exploration, and AI-driven solutions. This position requires the ability to analyze large and complex datasets, develop analytical models, and communicate findings that enable informed decision-making at both operational and strategic levels., * Bachelor's degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, Operations Research, Economics, or a related quantitative discipline. Additional relevant experience may be considered in lieu of a degree.

 * 10+ years of experience in data science, advanced analytics, statistical modeling, or machine learning.
 * Demonstrated experience developing and deploying predictive analytics and data-driven solutions in production environments.
 * Strong proficiency in Python, R, SQL, and data analysis techniques.
 * Experience with statistical analysis, predictive modeling, hypothesis testing, experimentation, and forecasting methodologies.
 * Experience working with large and complex datasets from multiple data sources.
 * Familiarity with machine learning frameworks such as Scikit-learn, TensorFlow, PyTorch, or similar technologies.
 * Experience with Generative AI, Large Language Models (LLMs), and AI-powered analytical solutions.
 * Strong understanding of data exploration, feature engineering, validation, and model evaluation techniques.
 * Proven ability to communicate complex analytical findings to executive leadership, mission stakeholders, and non-technical audiences.
 * Strong collaboration skills with experience working across multidisciplinary teams.

Eligibility Requirements:

 * US Citizenship with an at least Active Top Secret Clearance.

## Description

* Lead the development and implementation of advanced data science, analytics, and machine learning solutions supporting mission objectives.
 * Analyze large, structured, and unstructured datasets to identify patterns, trends, anomalies, and actionable insights.
 * Develop predictive, classification, forecasting, and optimization models to support mission and business decisions.
 * Apply statistical analysis, experimentation, and advanced analytical methodologies to solve complex operational challenges.
 * Develop and deploy Generative AI solutions, including Large Language Models (LLMs), prompt engineering, and AI-powered analytics capabilities.
 * Design and implement Retrieval-Augmented Generation (RAG) workflows and knowledge-driven analytical solutions.
 * Conduct data exploration, cleansing, feature engineering, validation, and model performance assessments.
 * Create visualizations, dashboards, reports, and executive briefings to communicate findings to technical and non-technical stakeholders.
 * Collaborate with data engineers, software developers, AI engineers, and mission partners to operationalize analytics solutions.
 * Ensure compliance with data governance, model governance, Responsible AI principles, and organizational standards.
 * Monitor model performance, fairness, explainability, and accuracy throughout the analytics lifecycle.
 * Serve as a trusted advisor and subject matter expert for data science and advanced analytics initiatives.