Data Scientist

Mantech International Corporation-posted about 2 months ago

Full-time • Mid Level

Hybrid • Ashburn, VA

5,001-10,000 employees

Professional, Scientific, and Technical Services

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Elevate your career with MANTECH International Corporation! Join a dynamic team dedicated to national security through cutting-edge technology. Since 1968, MANTECH has led in delivering advanced solutions to government intelligence, the Department of Defense, and Federal Civilian sectors. Dive into innovation in Digital Transformation, Cybersecurity, IT, Data Analytics and Software Development. Your journey to impactful work and rapid growth starts now-be extraordinary at MANTECH! MANTECH seeks a motivated, career- and customer-oriented Data Scientist SME to join our innovative team in Ashburn, VA. This is a hybrid position with 2 days onsite and 3 days remote. Each day U.S. Customs and Border Protection (CBP) oversees the massive flow of people, capital, and products that enter and depart the United States via air, land, sea, and cyberspace. The volume and complexity of both physical and virtual border crossings require the application of "big data" solutions to promote efficient trade and travel. Further, effective solutions help CBP ensure the movement of people, capital, and products are legal, safe, and secure.

Perform hands-on analysis and modeling involving the creation of intervention hypotheses and experiments, assessment of data needs and available sources, determination of optimal analytical approaches, performance of exploratory data analysis, and feature generation (e.g., identification, derivation, aggregation).
Collaborate with mission stakeholders to define, frame, and scope mission challenges where big data interventions may offer important mitigations and develop robust project plans with key milestones, detailed deliverables, robust work tracking protocols, and risk mitigation strategies.
Demonstrate proficiency in extracting, cleaning, and transforming CBP transactional and mission data associated within an identified problem space to build predictive models as well as develop appropriate supporting documentation.
Leverage knowledge of a variety of statistical and machine learning techniques and methods to define and develop programming algorithms; train, evaluate, and deploy predictive analytics models that directly inform mission decisions.
Execute projects including those intended to identify patterns and/or anomalies in large datasets; perform automated text/data classification and categorization as well as entity recognition, resolution and extraction; and named entity matching.
Brief project management, technical design, and outcomes to both technical and non-technical audiences including senior government stakeholders throughout the model development/project lifecycle through written as well as in-person reporting.

HS Diploma/GED and 20+ years or AS/AA and 18+ years or BS/BA and 12+ years or MS/MA/MBA and 9+ years or PhD/Doctorate and 7+ years
Experience in developing machine learning models and applying advanced analytics solutions to solve complex business problems.
Experience with programming languages including R, Python, Scala, Java, and proficiency with SQL programming. Experience performing data mining, analysis, and training set construction.
Experience constructing and executing queries to extract data in support of EDA and model development.
Proficiency with statistical software packages including SAS, SPSS Modeler, R, WEKA, or equivalent. Experience with pattern recognition and extraction, automated classification, and categorization.
Experience with entity resolution (e.g., record linking, named entity matching, deduplication/ disambiguation). Experience with unsupervised and supervised machine learning techniques and methods.
Proficiency with supervised machine learning methods including Decision Trees, Support Vector Machines, Logistic Regression, Random/Rotation Forests, Categorization/Classification, Neural Nets, Bayesian Networks, etc. Proficiency with unsupervised machine learning methods including Cluster Analysis (e.g., K-means, K-nearest Neighbor, Hierarchical, Deep Belief Networks, Principal Component Analysis), Segmentation, etc.

Experience with visualization tools and techniques (e.g., Periscope, Business Objects, D3, ggplot, Tableau, SAS Visual Analytics, PowerBI).
Experience with big data technologies (e.g., Hadoop, HIVE, HDFS, HBase, MapReduce, Spark, Kafka, Sqoop).
Master's degree in mathematics, statistics, computer science/engineering, or other related technical fields with equivalent practical experience.

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