Data Scientist

Data Scientist

MANTECH Raleigh, NC

International Corporation, seeks a motivated, career and customer-oriented Data Scientist to join our team in Raleigh, NC, Washington DC or Charlottesville, VA. This role is hybrid and must be onsite 1–2 days per week. This role is for a highly experienced and curious data scientist to lead and/or support the full-lifecycle development and deployment of analytical solutions at MANTECH. You will operate as a critical member of our analytics consulting team, acting as a trusted advisor for a diverse set of client partners. MANTECH's data scientists leverage advanced analytical methods to turn ambiguous business challenges into high-impact, production-ready solutions supported by robust data engineering practices.

Responsibilities Include But Are Not Limited To:

• Lead and Contribute to Data Science Projects: Dive deeply into complex and often nebulous requirements and draw from your expertise to develop production-ready models and analytics products for our clients. Clients' needs can require a broad set of analytics skills including but not limited to time series forecasting, natural language processing (NLP), classification/regression modeling, and optimization.
• Design Solutions within Client Environments: Adapt project work to the client's tooling and environment requirements. This will oftentimes require quickly learning new systems and tools to be able to deliver solutions that will work well within a client's ecosystem.
• Consult with Clients to Solve Problems: Engage in critical requirements gathering and data "sleuthing" to connect business use cases with the right analytics techniques to provide real-world impact. You will often work directly with clients to define and solve problems that lack clear initial definitions and will regularly communicate your solutions directly to stakeholders.
• Develop, Lead, and Support Analytics Training Courses: Develop and/or teach bespoke data science, AI, or related material designed specifically for Elder Research clients. This can include support for active courses through lecture, mentoring, and grading.
• Collaborate and Communicate: Work with internal teams and external client stakeholders to effectively communicate technical findings, maintain strong relationships, and ensure delivery aligns with overall objectives.

Minimum Qualifications:

• 3+ years of experience as a Data Scientist with a Bachelor of Science degree in a relevant field such as statistics, computer science, economics, mathematics, analytics, data science, business, or the social or natural sciences.
• High proficiency in Python and/or R, including libraries for data analysis and modeling.
• Experience developing machine learning models and employing model validation best practices.
• Strong SQL experience to facilitate deep dives and analysis of complex datasets.
• Working knowledge of version control (Git) for source code.
• Initiative and willingness to "hit the ground running" on new projects; unafraid to work independently.
• Passion and curiosity for exploring data to understand the business context and connect technical work to real-world impact.
• Excitement to continually learn new tools and techniques.
• Humility and eagerness to collaborate with other data scientists, data engineers, engagement and project managers, and clients.
• Ability to communicate complex technical findings to a variety of audiences, including those with limited data science experience.

Preferred Qualifications:

• Exceptional comfort with ambiguity and a proven ability to define and drive projects from vague concepts to concrete outcomes.
• Strong consultative and communication skills to represent the team effectively to clients.
• Ability to work autonomously in fully remote, flexible client environments.
• Advanced degree (MS or PhD) in statistics, computer science, data science, mathematics, analytics, engineering, or related fields.
• Experience applying advanced statistical concepts and data science workflows to real-world data, including out-of-sample validation for various model types, sampling considerations, bias detection, handling missing or outlying data, and robust exploratory analysis.
• Understanding of the data analytics lifecycle (e.g., CRISP-DM).