Data Scientist - Experienced to Expert Level (Maryland)

Responsibilities

• Exploring data analysis and model-fitting to reveal data features of interest
• Using the machine-learned predictive modeling
• Constructing usable data sets from multiple sources to meet customer needs
• Identifying and analyzing anomalous data (including metadata)
• Developing conceptual design and models to address mission requirements
• Developing qualitative and quantitative methods for characterizing datasets in various states
• Performing analytic modeling, scripting, and/or programming
• Working collaboratively and iteratively throughout the data-science lifecycle
• Designing and developing analytics and techniques for analysis
• Analyzing data using mathematical and statistical methods
• Evaluating, documenting, and communicating research processes, analyses, and results to customers, peers, and leadership
• Creating interpretable visualizations

Qualifications

• The qualifications listed are the minimum acceptable to be considered for the position.
• Applicants will be asked to complete the Data Science Examination (DSE) which evaluates their knowledge of statistics, mathematics, and computer science topics that pertain to data science work.
• Passing this examination at a local testing site is a requirement in order to be considered for selection into a data scientist position.
• Upon passing the examination, applicants will be evaluated for the minimum qualifications outlined in this ad.
• Transcripts for each academic institution are required prior to being invited to interview with Agency data science professionals and should be submitted as part of the online application.
• Unofficial transcripts are fine at this stage.
• Note that different degree fields have different requirements as described below.
• Degrees in Mathematics, Applied Mathematics, Statistics, Applied Statistics, Data Science, Operations Research, Quantitative/Computational Finance, Econometrics/Quantitative Economics, Computer Science, or Computer Engineering qualify without additional coursework or a Data Science certificate.
• Degrees in Engineering, Physical Sciences, Mathematical Biology/Bioinformatics, Life Sciences, Environmental Science, Data Analytics, or Information Science/Systems/Technology, must include either a Data Science certificate from an accredited college/university OR 5 or more courses in advanced mathematics (for example, calculus, differential equations, discrete mathematics, linear algebra, and calculus-based statistics) and/or advanced computer science (for example, algorithms, programming, data structures, data mining, artificial intelligence).
• Other degrees must be accompanied by a Data Science certificate from an accredited college/university, and must include a total of 5 or more courses in advanced mathematics AND advanced computer science.
• At least one course must be from advanced mathematics (for example, calculus, differential equations, discrete mathematics, linear algebra, and calculus-based statistics).
• At least one course must be from advanced computer science (for example, algorithms, programming, data structures, computer architecture, data mining, artificial intelligence).
• Experience must include both programming and one or more of the following: designing/implementing machine learning, data mining, statistical analysis, statistical consulting, artificial intelligence development, computational science, software engineering, technical writing, data visualization, or data engineering.
• Experience must also include formal or informal leadership.

Competencies

• The ideal candidate has a desire for continual learning along with excellent analytical, problem-solving, communication (oral and written), and interpersonal skills who is:
• Accountable
• Proactive
• Detail oriented
• Able to solve complex problems
• Proficient with critical thinking and reasoning to make analytic determinations
• Effective at working in a collaborative team environment
• Able to bridge the gap with both technical and non-technical audiences
• Able to provide technical leadership in formal or informal roles
• Knowledge, skills, and relevant experience in programming, formal or informal leadership, and one or more of the following is required:
• Designing and implementing machine learning
• Data mining
• Statistical analysis
• Statistical consulting
• Artificial Intelligence development
• Computational science
• Software engineering
• Technical writing
• Data visualization
• Data engineering