Job Description
About the position Bowman has an opportunity for a Data Scientist/Machine Learning Engineer to 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. 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. 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. 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 Apply tot his job