Data Scientist (Mid/Senior) - TS/SCI

Wiser•Springfield, VA

2d•Onsite

About The Position

Data Scientist - TS/SCI Cleared Location: Springfield, VA Required Clearance: Top Secret/SCI Security Clearance Wiser offers innovative solutions to clients in the public, private, and government sectors. We combine technology and expertise to develop inventive solutions that deliver quality results and aid in critical decision making. With the flexibility and efficiency of a small business, we provide nimble responsiveness with the low risk and strong performance experience of an established GEOINT and Geospatial service provider.

Requirements

U.S. Citizen
Active Top-Secret/SCI security clearance at time of application and willingness to complete a CI poly upon request.
3-10 years of programming skills with the ability to write / maintain scripts, including Python and JAVA scripts and familiarity of querying with SQL.
Advanced knowledge in data science, including the areas of data services, modeling, and analytics.
Advanced knowledge of geospatial data management including data type conversion; coordinate systems (latitude and longitude, UTM) and their conversions; and knowledge of projections and their properties / conversions.
Minimum 2 years of experience working with data quality control tools including ArcGIS Data ReViewer.
Proficient with ESRI Workflow Manager WMX and TAM.
Senior candidates require additional experience with artificial intelligence, natural language processing, machine-to-machine learning, and NoSQL including document and graph schemas, ontologies, OWL, and data base inferencing.

Nice To Haves

Experience with combining digital cartography, computer technology, GIS, cartographic and geospatial production techniques, remote sensing, photogrammetry, and digital data formats.
Ability to clean / prune data to discard irrelevant information
Ability to examine data from a variety of angles to determine hidden value, weaknesses, trends, and/or opportunities
Advanced knowledge of ESRI ArcGIS and ArcServer
Knowledge of database systems and architecture (ORACLE, PostgresEQL, NoSEL (MongoDB), Microsoft Access)
Ability writing SQL (and NoSQL for senior candidates)
Understanding cloud architecture, infrastructure, services (including geospatial specific microservices), and DevOps
Knowledge of symbolization rules (how symbols are used to portray features)
Knowledge of generalization rules
Experience working with geospatial data in a multi-user enterprise environment (i.e., versioning data)
Knowledge of artificial intelligence, natural language processing, and machine-to-machine learning
Knowledge of Metrics dissemination
Ability to convert unstructured data into structured data
Proficient in MS Outlook, Word, PowerPoint, Access, and Excel
Senior candidates may have additional knowledge of Data Warehousing, Data Mining, Predictive Modeling, Data Integrity / Security, Data Anomaly Detection, Statistical Analysis, S-57 data structure, specifications, validation, and the ability to produce ENC and AML, use of ECDIS display systems