Description
Leidos is looking for a Data Scientist to support a large U.S. Department of Justice (DOJ) program. The Antitrust Division of the U.S. Department of Justice (DOJ) is responsible for enforcing federal antitrust laws and promoting fair competition in the marketplace. As a Data Scientist, you will play a pivotal role in advancing the research efforts by applying your expertise in data analytics, machine learning, and computational methods to extract meaningful insights from large, complex datasets. Your work will support innovative research in areas such as artificial intelligence (AI), information systems, computational models, and data-driven discoveries. You will collaborate with multidisciplinary teams, including researchers, engineers, and domain experts, to design and implement data science solutions that push the boundaries of knowledge and technology. This work is located onsite in the DC area.

QUALIFICATIONS:

• Master’s or PhD in Data Science, Computer Science, Statistics, Mathematics, or a related field and 8+ years of relevant experience. Additional years of experience will be considered in lieu of a degree.
• 5+ years of hands-on experience in data science and computer research, with at least 3 years working in a research-focused environment.
• Advanced knowledge of machine learning algorithms, deep learning frameworks, and statistical modeling.
• Proficiency in programming languages such as Python, R, Julia, or MATLAB for developing and testing models.
• Experience with data visualization tools (e.g., Matplotlib, Seaborn, Tableau, Power BI) to present insights effectively.
• Strong understanding of big data technologies (e.g., Hadoop, Spark) and cloud platforms (AWS, Azure, Google Cloud).
• Familiarity with NLP, computer vision, or other specialized techniques relevant to computer and information research.
• Experience with version control systems (e.g., Git) and software development practices.
• Strong background in applying data science to domains such as AI, computational research, data mining, or information systems.
• Excellent problem-solving skills and the ability to think critically and analytically to address complex research challenges.
• Strong verbal and written communication skills, with the ability to convey technical concepts to both technical and non-technical audiences.
• Ability to work effectively in a multidisciplinary, collaborative environment and mentor junior researchers and scientists.

DESIRABLE SKILLS:

• Experience with advanced topics in reinforcement learning, neural networks, or graph theory as they apply to computational research.
• Familiarity with distributed computing and parallel processing techniques for handling large-scale datasets.
• Contribution to open-source projects or participation in relevant data science communities.

• Lead data-driven research by applying sophisticated statistical, machine learning, and computational methods to analyze complex datasets related to computer and information science.
• Develop and implement predictive models, classification algorithms, and clustering techniques to support research goals.
• Apply natural language processing (NLP), computer vision, or other domain-specific algorithms as required by the research.
• Design, develop, and optimize advanced algorithms that can process large-scale data efficiently, with a focus on performance and scalability.
• Innovate and test new computational techniques to improve the accuracy and robustness of models for research applications.
• Contribute to algorithmic advancements in the context of AI, machine learning, and deep learning.
• Stay updated on the latest academic research and industry advancements in data science, AI, and information systems, and apply relevant findings to ongoing projects.
• Work closely with data engineers to build and optimize data pipelines that facilitate the processing and analysis of large datasets.
• Utilize cloud platforms and big data technologies (e.g., AWS, Azure, Hadoop, Spark) for efficient data processing and model deployment.
• Design and implement robust data storage, retrieval, and management strategies for research datasets.
• Create compelling data visualizations and reports that convey complex research findings in a clear and accessible manner to both technical and non-technical stakeholders.
• Share knowledge of best practices in data science, modeling, and computational techniques within the organization.