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Data Scientist
| Company | Leidos |
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| Location | Washington, DC, USA |
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| Salary | $104650 – $189175 |
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| Type | Full-Time |
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| Degrees | Master’s, PhD |
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| Experience Level | Senior, Expert or higher |
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Requirements
- 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.
Responsibilities
- 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.
Preferred Qualifications
- 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.
