Data Scientist Technical Specialist

Arcfield•McLean, VA

About The Position

The Data Scientist works on unusually complex technical problems and provides highly innovative solutions. The role determines and pursues courses of action needed to achieve desired results. It requires strong technical and computational skills in engineering, physics, and mathematics, together with the ability to code, design, develop, and deploy sophisticated applications using advanced unstructured and semi-structured data analysis techniques in high-performance computing environments. The Data Scientist uses advanced tools and computational skills to interpret, connect, predict, and make discoveries in complex data, delivering recommendations for business and analytic decisions. The position supports mission and targeting needs by discovering uses for new data sets, identifying and exploiting relevant data streams, integrating data sources, and developing real-time processing techniques and user-friendly interfaces. It involves analysis of high-volume collections and high-velocity streams, development of signature-detection techniques, and transformation of large data sets for geospatial analysis and visualization. The role also collaborates with government, contractor, and non-IC partner personnel and supports customers with varying requirements.

Requirements

Must possess and be able to maintain a TS/SCI clearance with polygraph.
Bachelor’s degree or equivalent experience in Computer Science, Information Systems, Engineering, Business, or a related scientific or technical discipline.
Experience level: BS 10–12 years, MS 8–10 years, or PhD 5–7 years.
Knowledge of the Sponsor’s environment.
Experience working with sensitive commercial partners and commercial data sets.
Experience presenting data analysis results to drive technical development and inform decision-making.
Experience with software development environments, including open-source enterprise stacks such as Java, Linux, Ruby, or Python, and Windows stacks such as .NET, C#, or C++.
Experience with data transport and transformation technologies and APIs such as JSON, XML, XSLT, JDBC, SOAP, or REST.
Experience with cloud-based and distributed data processing and analytics technologies such as Hadoop, Mahout, Accumulo, Hive, Impala, or Pig.
Experience with visual analytics tools such as Microsoft Pivot, Palantir, or other visual analytics platforms.
Experience with open-source text processing and search technologies such as Lucene, Sphinx, Nutch, or Solr.
Experience with entity extraction, conceptual search, and semantic analysis techniques such as Latent Semantic Indexing (LSI) and Latent Dirichlet Allocation (LDA).
Experience with machine learning, algorithm development and analysis, and data clustering techniques.
Knowledge of data science tools, techniques, and methodologies.

Nice To Haves

Ability to work with data sets across different computer systems, networks, and organizations.
Strong interpersonal and communication skills.
Ability to work effectively with cross-functional teams and stakeholders.

Responsibilities

Discover new utilities for data sets, develop innovative targeting methodologies, and collaborate with internal and external stakeholders on data-driven solutions for the Sponsor’s mission needs.
Identify, assess, and exploit relevant data streams to support mission and targeting requirements.
Design and implement real-time data processing techniques.
Develop user-friendly data interfaces for stakeholders, including targeters and engineers.
Integrate multiple data streams to create a unified view of complex data sets.
Establish data science techniques to detect identified signatures in large data sets.
Leverage diverse data sources to enhance new data sources.
Support multiple high-priority customers with varying requirements.
Collaborate with government, contractor, and non-IC partner personnel.
Troubleshoot data processing issues across networks and optimize data delivery and formats.
Engage with stakeholders to develop solutions based on prioritized requirements.
Analyze unstructured and semi-structured data using techniques such as latent semantic indexing, entity identification and tagging, complex event processing, and algorithms on distributed, clustered, and cloud-based high-performance infrastructures.
Apply non-traditional approaches to large-scale analysis of unstructured data for high-value use cases visualized through multidimensional interfaces.
Handle, process, and index requests against high-volume data collections and high-velocity data streams.
Transform large data sets into formats suitable for geospatial analysis and visualization.