Data Scientist

OPENDataJobs

Data Scientist turning complex data into evidence-based models and analyses for OPEN Data Jobs’ federal-sector clients. Building reproducible pipelines and communicating uncertainty to decision-makers.

Posted 8/8/2026full-timeWashington • District of Columbia, Washington • 🇺🇸 United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in statistical analysis, machine learning, and data exploration, with a strong focus on data quality and reproducibility. Proficient in communicating complex findings to diverse audiences and collaborating with stakeholders to drive data-informed decisions.

Highest-signal resume keywords

Statistical AnalysisMachine LearningData ExplorationPython ProgrammingReproducible Practices

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

StatisticsProbabilityResearch DesignOptimizationData AnalysisExperimental DesignForecastingNatural Language ProcessingCausal AnalysisGeospatial Analysis

Soft Skills

CommunicationCollaborationAttention to Detail

Tools & Technologies

PythonRSQLStatistical PackagesML LibrariesCloud Analytical EnvironmentsVisualization Platforms

Industry Keywords

Data QualityModel ValidationAnalytical PipelinesModel Performance AssessmentBias Interpretation

Tech Stack

Tools & technologies

CloudPythonSQL

About the role

Key responsibilities & impact

Define measurement needs and assess whether available data supports stakeholder questions
Select statistical and machine learning methods appropriate to the question and evidence
Conduct exploratory data analysis covering distributions, relationships, outliers, missingness, and data limitations
Develop forecasting, classification, risk, anomaly-detection, segmentation, causal, simulation, or optimization models
Design experiments or quasi-experiments, sampling plans, measurement strategies, and evaluation frameworks
Validate assumptions, compare alternatives and models, investigate errors, and quantify uncertainty
Examine data quality and bias and interpret results in context
Build reproducible analytical pipelines, notebooks, code, data documentation, model cards, and validation reports
Communicate findings, uncertainty, assumptions, limitations, and appropriate uses to technical and nontechnical audiences
Collaborate with subject-matter experts, analysts, engineers, stakeholders, and decision-makers
Help define ongoing model performance assessment when models are used repeatedly

Requirements

What you’ll need

Working foundation in statistics, probability, research design, machine learning, optimization, or another relevant quantitative discipline
Ability to prepare, explore, and analyze data using Python, R, or SQL
Attention to data quality and provenance
Experience selecting methods, validating assumptions, comparing models, investigating errors, and interpreting results in context
Reproducible practices using documented code, version control, peer review, traceable data transformations, and clear analytical records
Ability to communicate uncertainty, bias, limitations, and appropriate use clearly
Specific openings may require statistical inference, experimental design, forecasting, natural language processing, computer vision, econometrics, operations research, causal analysis, geospatial analysis, model risk, program evaluation, or applied AI
Specific openings may require programming languages, statistical packages, ML libraries, cloud analytical environments, distributed-computing tools, domain datasets, visualization platforms, or documentation and review standards

Benefits

Comp & perks

Compensation, benefits, work location, and employment terms are set for each specific opening and will be stated with that opening