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The work
Data Scientists turn difficult questions and complex data into evidence. They work with stakeholders to define what should be measured, determine whether the available data can support the question, select appropriate statistical or machine learning methods, and explain what the results do and do not establish.
The work runs from exploratory analysis through model development, validation, interpretation, and communication. Data Scientists examine data quality and bias, test assumptions, quantify uncertainty, compare alternatives, document methods, and make analyses reproducible. When a model will be used repeatedly, they help define how its performance should be assessed over time.
What you'll build
路聽聽聽聽聽聽聽 Forecasting, classification, risk, anomaly-detection, segmentation, causal, simulation, or optimization models matched to the question and available evidence.
路聽聽聽聽聽聽聽 Exploratory analyses that reveal distributions, relationships, outliers, missingness, and limits in the data before formal modeling begins.
路聽聽聽聽聽聽聽 Experimental or quasi-experimental designs, sampling plans, measurement strategies, and evaluation frameworks.
路聽聽聽聽聽聽聽 Reproducible analytical pipelines, notebooks, code, data documentation, model cards, and validation reports that allow others to follow the work.
路聽聽聽聽聽聽聽 Decision briefings and analytical products that communicate results, uncertainty, assumptions, limitations, and appropriate uses to technical and nontechnical audiences.
Who you are
You are rigorous about methods and candid about uncertainty. You would rather narrow a claim than overstate the evidence, and you are willing to report a null or inconvenient result when that is what the analysis supports.
You are curious about the domain, not only the dataset. You work with subject-matter experts, analysts, engineers, and decision-makers to frame the right question, challenge assumptions, and turn technical results into conclusions people can use responsibly.
What you bring
路聽聽聽聽聽聽聽 A working foundation in statistics, probability, research design, ML, optimization, or another quantitative discipline relevant to the opening.
路聽聽聽聽聽聽聽 Ability to prepare, explore, and analyze data using tools such as Python, R, or SQL, with attention to quality and provenance.
路聽聽聽聽聽聽聽 Experience selecting methods, validating assumptions, comparing models, investigating errors, and interpreting results in context.
路聽聽聽聽聽聽聽 Reproducible practice through documented code, version control, peer review, traceable data transformations, and clear analytical records.
路聽聽聽聽聽聽聽 The communication judgment to explain uncertainty, bias, limitations, and appropriate use without hiding the central finding.
About OPEN Data Jobs
OPEN Data Jobs connects AI, data, and software professionals with critical roles, primarily in the federal sector. Registering with ODJ can put your profile in view for multiple positions across several clients.
Register for Data Scientist Role
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What openings may require
An opening may emphasize statistical inference, experimental design, forecasting, natural language processing, computer vision, econometrics, operations research, causal analysis, geospatial analysis, model risk, program evaluation, or applied AI. Production deployment may be shared with Machine Learning Engineers or AI Engineers.
Specific openings may name programming languages, statistical packages, ML libraries, cloud analytical environments, distributed-computing tools, domain datasets, visualization platforms, or documentation and review standards. OPEN Data Jobs will state the required methodological and domain depth with each opening.
Compensation, benefits, work location, and employment terms are set for each specific opening and will be stated with that opening
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