
Data Scientist/Machine Learning Engineer - Reston, VA
- Reston, VA
- Permanent
- Full-time
- 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.
- Analytical rigor and strong statistical and machine learning expertise.
- Ability to communicate complex technical concepts to diverse audiences.
- Business curiosity and ability to align technical work to enterprise priorities.
- Attention to detail, experimentation mindset, and problem-solving skills.
- Collaboration and adaptability in a cross-functional innovation team.
- High degree of discretion and ability to manage highly confidential information.
- Highly motivated and problem-solving attitude.
- Strong sense of urgency in responding to constituents.
- Effective verbal and written communication skills.
- Strong work ethic and commitment to quality.
- Self-reliance and ability to operate independently with limited direction.
- Commitment to promoting the reputation of the company through quality of work.
- Aspirations to grow professionally and advance within the company.
- Effective working relationship with internal leaders and peers, as well as external clients.
- Commitment to becoming a “citizen” of the broader organization, breaking down barriers and silos.
- Commitment to working in partnership with others inside and outside the organization.
- Ability to effectively manage multiple time-sensitive tasks.
- 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.
- Familiarity with large language models, NLP, or AI-assisted tools is a plus.
- Primarily in-person or hybrid work setting based on business needs.
- Minimal travel required (approximately 10%).
- Professional office environment which may include bright/dim light, noise, fumes, odors, and traffic.
- Frequent and prolonged use of standard office equipment such as computers, phones, photocopiers, etc.
- 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