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).
• Passing this examination is a requirement to be considered for selection into a data scientist position.
• Transcripts for each academic institution are required prior to being invited to interview.
• 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

• Designing and implementing machine learning
• Data mining
• Statistical analysis
• Statistical consulting
• Artificial Intelligence development
• Computational science
• Software engineering
• Technical writing
• Data visualization
• Data engineering