Data Scientist/Machine Learning Engineer - Reston, VA

Bowman Consulting Group • Reston, VA

137d • Hybrid

About The Position

Bowman has an opportunity for a Data Scientist/Machine Learning Engineer to join our team in Reston, VA. At Bowman, we believe in creating opportunities for aspiring people to thrive and achieve ambitious goals. That's why a career at Bowman is more than a job. It is an opportunity to be part of a diverse and engaged community of professionals, to be treated as a respected and valued member of a motivated team and to be empowered to do exceptional work that advances the best interest of everyone involved. We recognize the importance of creating a work environment that is both rewarding to our employees and supportive of our unwavering commitment to provide unparalleled service to our clients. The Data Scientist / Machine Learning (ML) Engineer supports the organization's AI and automation goals by developing, training, and deploying machine learning models and data-driven solutions. This role enables the transformation of business challenges into actionable insights and intelligent applications by analyzing large datasets, building predictive models, and collaborating on AI/ML product initiatives. The Data Scientist / ML Engineer works closely with stakeholders to prototype, test, and scale machine learning use cases that improve internal operations and support external customer offerings. This role plays a critical part in shaping the company's data science capabilities, ensuring analytical rigor, and deploying models that enhance business outcomes, automate manual processes, and contribute to digital innovation.

Requirements

• Bachelor's degree in Computer Science, Data Science, Statistics, Engineering, or related field; advanced degree preferred.
• Minimum of five (5) years of experience in data science or machine learning engineering.
• Strong programming skills in Python and familiarity with SQL, R, or other data languages.
• Experience with data visualization tools (e.g., Power BI, Tableau, matplotlib).
• Proficiency in ML tools and platforms such as scikit-learn, TensorFlow, PyTorch, or Azure ML.
• Experience working with cloud environments, version control systems, and MLOps tools.

Nice To Haves

• Familiarity with large language models, NLP, or AI-assisted tools is a plus.

Responsibilities

• Partner with product, engineering, and analytics teams to translate business needs into machine learning solutions.
• Contribute to the organization's AI/ML strategy and model development roadmap.
• Collaborate with internal teams to identify data sources, develop data pipelines, and validate models.
• Analyze business workflows to uncover opportunities for predictive modeling, AI-powered optimization, and automation.
• Support and mentor analysts and developers in applying machine learning techniques to real-world challenges.
• Participate in cross-functional innovation and product development initiatives.
• Share findings and insights with business stakeholders to support data-driven decision making.
• Design, build, and deploy machine learning models and data pipelines across structured and unstructured datasets.
• Perform data wrangling, feature engineering, and exploratory data analysis to uncover patterns and model features.
• Conduct hypothesis testing, A/B testing, and apply statistical and predictive modeling techniques.
• Train and optimize supervised, unsupervised, and deep learning models for performance, scalability, and generalization.
• Work with ML frameworks and cloud services (e.g., Azure ML, TensorFlow, PyTorch, scikit-learn).
• Develop MLOps practices for monitoring, logging, and retraining deployed models.
• Support the integration of models into enterprise platforms, APIs, and front-end applications.
• Create and maintain reusable code libraries, templates, and automation scripts to streamline the ML development lifecycle.
• Collaborate with DevOps and Infrastructure teams to build scalable model deployment pipelines.
• Design and implement performance monitoring and alerting systems for production ML models.
• Continuously evaluate model effectiveness and retrain as needed using feedback loops and real-world data.
• Ensure all work aligns with security, privacy, and compliance standards related to data handling and model governance.
• Document methodology, code, experiments, and model performance metrics to ensure transparency, reproducibility, and collaboration.

Benefits

• Medical, dental, vision, life, and disability insurance
• 401(k) retirement savings plan with company match
• Paid time off, sick leave, and paid holidays
• Tuition reimbursement and professional development support
• Discretionary bonuses and other performance-based incentives
• Employee Assistance Program (EAP), wellness initiatives, and employee discounts

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Industry: Professional, Scientific, and Technical Services
• Education Level: Bachelor's degree
• Number of Employees: 1,001-5,000 employees