Source Fly
Data Scientist
Source Fly, Richmond, Virginia, United States,
Senior Data Scientist
We are seeking capable, qualified, and versatile Data Scientists to help lead the development and delivery of high-quality predictive modelling solutions.
Within three to six months of joining the project, Data Scientists will be expected to:
Lead and 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.
Qualifications
Education:
Bachelor’s Degree (required) in operations research, industrial engineering, mathematics, statistics, computer science/engineering, or other related technical fields with equivalent practical experience.
Required Qualifications
7-12 years of relevant experience
Experience in applying advanced analytics solutions to solve complex business problems
Experience with programming languages including: R, Python, JavaScript, Visual Basic
Experience with creating VBA applications and macros to structure, manage, and wrangle key datasets
Experience with core data science libraries – Pandas, NumPy, Matplotlib, Plotly, etc.
Experience with Anaconda distribution of Python for package management and deployment
Familiarity with command-line shell programming (Powershell, cmd, etc.)
Proficiency with SQL programming
Familiarity with RESTful APIs, web scraping, and processing unstructured data
Knowledge of visualization and presentation techniques including Tableau, Power BI, Jupyter Notebooks, etc.
Knowledge of cloud technologies such as AWS or Google
Proficiency using git for version control, collaboration, and code review
Familiarity with software organization tools and frameworks (Docker, virtual environments, etc.)
Experience with engineering and development collaboration tools such as Jira and Confluence.
Experience with Natural Language Processing (NLP), computational linguistics, Entity extraction, named entity recognition (NER), name matching, disambiguation.
Experience constructing and executing queries to extract data in support of EDA and model development
Experience with unsupervised and supervised machine learning techniques and methods
Experience working with large-scale (e.g., terabyte and petabyte) unstructured and structured data sets and databases
Experience performing data mining, analysis, and training set construction
Desired Qualifications
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.
Proficiency with Supervised Machine Learning methods including Decision Trees, Support Vector Machines, Logistic Regression, Random/Rotation Forests, Categorization/Classification, Neural Nets, Bayesian Networks, etc.
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 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
Active Top Secret
Clearance:
Selected applicants must be a US Citizen and able to obtain and maintain a U.S. Customs and Border Protection (CBP) suitability.
#J-18808-Ljbffr
We are seeking capable, qualified, and versatile Data Scientists to help lead the development and delivery of high-quality predictive modelling solutions.
Within three to six months of joining the project, Data Scientists will be expected to:
Lead and 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.
Qualifications
Education:
Bachelor’s Degree (required) in operations research, industrial engineering, mathematics, statistics, computer science/engineering, or other related technical fields with equivalent practical experience.
Required Qualifications
7-12 years of relevant experience
Experience in applying advanced analytics solutions to solve complex business problems
Experience with programming languages including: R, Python, JavaScript, Visual Basic
Experience with creating VBA applications and macros to structure, manage, and wrangle key datasets
Experience with core data science libraries – Pandas, NumPy, Matplotlib, Plotly, etc.
Experience with Anaconda distribution of Python for package management and deployment
Familiarity with command-line shell programming (Powershell, cmd, etc.)
Proficiency with SQL programming
Familiarity with RESTful APIs, web scraping, and processing unstructured data
Knowledge of visualization and presentation techniques including Tableau, Power BI, Jupyter Notebooks, etc.
Knowledge of cloud technologies such as AWS or Google
Proficiency using git for version control, collaboration, and code review
Familiarity with software organization tools and frameworks (Docker, virtual environments, etc.)
Experience with engineering and development collaboration tools such as Jira and Confluence.
Experience with Natural Language Processing (NLP), computational linguistics, Entity extraction, named entity recognition (NER), name matching, disambiguation.
Experience constructing and executing queries to extract data in support of EDA and model development
Experience with unsupervised and supervised machine learning techniques and methods
Experience working with large-scale (e.g., terabyte and petabyte) unstructured and structured data sets and databases
Experience performing data mining, analysis, and training set construction
Desired Qualifications
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.
Proficiency with Supervised Machine Learning methods including Decision Trees, Support Vector Machines, Logistic Regression, Random/Rotation Forests, Categorization/Classification, Neural Nets, Bayesian Networks, etc.
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 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
Active Top Secret
Clearance:
Selected applicants must be a US Citizen and able to obtain and maintain a U.S. Customs and Border Protection (CBP) suitability.
#J-18808-Ljbffr