Data Scientist

Jobtailor; Washington (District of Columbia); On-site; USD 110,000 - 170,000; Full time

Job summary

Jobtailor is seeking a data science professional to lead analytical work, using statistics, ML, and data exploration to answer stakeholder questions. You will design experiments, validate models, and ensure reproducible workflows across teams.

The role emphasizes uncertainty quantification, bias interpretation, and communicating results to both technical and non-technical audiences, with collaboration across SMEs, engineers, and decision-makers.

Qualifications

• 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

Responsibilities

• 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

Skills

Statistics

Probability

Research design

Optimization

Data analysis

Experimental design

Forecasting

Natural language processing

Causal analysis

Geospatial analysis

Model validation

Education

Bachelor's degree in statistics/math/computer science

Tools

Python

R

SQL

Statistical Packages

ML libraries

Cloud analytical environments

Visualization platforms

Job description

• 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

• 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

Core Competencies

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 Analysis


• Machine Learning


• Data Exploration


• Python Programming


• Reproducible Practices

ATS Optimization Keywords

Hard Skills

• Statistics


• Probability


• Research Design


• Optimization


• Data Analysis


• Experimental Design


• Forecasting


• Natural Language Processing


• Causal Analysis


• Geospatial Analysis

Soft Skills

• Communication


• Collaboration


• Attention to Detail

Industry Keywords

• Data Quality


• Model Validation


• Analytical Pipelines


• Model Performance Assessment


• Bias Interpretation

Tools & Technologies

• Python


• R


• SQL


• Statistical Packages


• ML Libraries


• Cloud Analytical Environments


• Visualization Platforms