Data Scientist/Machine Learning Engineer - Reston, VA

Overview

Develop, train, and deploy ML models and pipelines for business insights.

Key Responsibilities

• model development
• data pipelines
• mlops
• performance monitoring
• feature engineering
• stakeholder communication

Tasks

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

Requirements

• python
• tensorflow
• pytorch
• power bi
• mlops
• bachelor's

What You Bring

-Collaboration and adaptability in a cross-functional innovation team. -Ability to effectively manage multiple time-sensitive tasks. -Business curiosity and ability to align technical work to enterprise priorities. -Strong sense of urgency in responding to constituents. -Self-reliance and ability to operate independently with limited direction. -Minimum of five (5) years of experience in data science or machine learning engineering. -Highly motivated and problem-solving attitude. -Effective working relationship with internal leaders and peers, as well as external clients. -Frequent and prolonged use of standard office equipment such as computers, phones, photocopiers, etc. -Bachelor’s degree in Computer Science, Data Science, Statistics, Engineering, or related field; advanced degree preferred. -Attention to detail, experimentation mindset, and problem-solving skills. -Analytical rigor and strong statistical and machine learning expertise. -Experience with data visualization tools (e.g., Power BI, Tableau, matplotlib). -Familiarity with large language models, NLP, or AI-assisted tools is a plus. -Proficiency in ML tools and platforms such as scikit-learn, TensorFlow, PyTorch, or Azure ML. -Minimal travel required (approximately 10%). -Experience working with cloud environments, version control systems, and MLOps tools. -Strong programming skills in Python and familiarity with SQL, R, or other data languages. -Effective verbal and written communication skills. -Strong work ethic and commitment to quality. -Ability to communicate complex technical concepts to diverse audiences. -High degree of discretion and ability to manage highly confidential information.

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Benefits

-Discretionary bonuses and other performance-based incentives -Tuition reimbursement and professional development support -Employee Assistance Program (EAP), wellness initiatives, and employee discounts -401(k) retirement savings plan with company match -Primarily in-person or hybrid work setting based on business needs. -Medical, dental, vision, life, and disability insurance -Professional office environment which may include bright/dim light, noise, fumes, odors, and traffic. -Paid time off, sick leave, and paid holidays

About Bowman Consulting

-Founded with a vision to provide expert consulting and engineering solutions for infrastructure projects. -Delivers a wide array of services including civil engineering, environmental, and land development. -Works across diverse sectors such as energy, transportation, and residential developments. -Specializes in large-scale infrastructure projects, often involving complex regulatory and design challenges. -Has a strong history of serving both public and private sector clients, with a focus on sustainable growth. -Notable for their ability to adapt and innovate in rapidly evolving industries like renewable energy. -Known for their collaborative approach, ensuring that projects are delivered efficiently and with precision.

Sector Specialisms