[Remote] Data Scientist

Note: The job is a remote job and is open to candidates in USA. ISF, Inc. is seeking an independent contractor for the role of Quantitative Reviewer to support a public health analytics project. The primary responsibilities include reviewing predictive model specifications, verifying analytical outputs, and ensuring the accuracy and consistency of documentation and model outputs for a state-level decision-support system.

Responsibilities

• Read and evaluate the statistical methods described in the project’s technical framework document to assess whether the model specification is internally consistent, the assumptions are appropriate, and the described approach is correctly implemented
• Flag methodological concerns, specification errors, or inconsistencies between the described methods and standard practice in Bayesian spatial modeling or public health surveillance
• Check that model output values are plausible, internally consistent, and correctly reported in tables and figures, including latent population estimates, detection probabilities, geographic risk scores, treatment effect estimates, convergence diagnostics, and scenario projections
• Verify that numbers cited in the technical document match the underlying model output files, and that calculations (rates, percentages, aggregations, credible intervals) are arithmetically correct
• Review sections of the technical deliverable as they are updated to confirm that numerical values, statistical summaries, table entries, and methodological descriptions accurately represent the underlying analytical work
• Identify any places where results are mischaracterized, ambiguously described, or where the documentation does not match model outputs
• Provide written review comments for the lead scientist to address
• Following data refreshes of the project’s Azure-hosted decision-support tool, spot-check displayed values (county-level counts, rates, projections, and KPI figures) against source model output files to confirm the tool is correctly reflecting updated results
• Provide time range estimates for requested QA outputs to the Principal within 24 hours of tasking
• Provide weekly updates regarding work completed to the Principal
• Provide objective feedback related to the predictive model and its outputs to the Principal, advising on future scoping with the client as appropriate

Skills

• Education: Doctorate (Ph.D.) in biostatistics, statistics, health data science, epidemiology, or closely related quantitative field
• Experience translating highly technical concepts with simplicity and accuracy to non-specialist audiences
• Experience accurately estimating the time required to complete tasks
• Experience advising leadership regarding technical processes, outputs, and hours required to complete scope
• Must be proficient in R, Azure, Azure Databricks, Claude Code
• Must currently hold, or have the ability to obtain, CITI certification to access restricted data
• Strong numeracy: the ability to catch arithmetic errors, implausible values, and internal inconsistencies in tables of model results is a core requirement
• Deep comfort with coding is important for this role. The work involves reading, running, and evaluating R scripts across a complex multi-source analytical pipeline, and the ability to move through code confidently is central to the QA function
• Ability to assess a defined scope of work and offer a reasonable hour estimate before beginning
• Comfort surfacing scope questions and clarifying tasks early
• Experience tracking and reporting hours on consulting or contract work
• Client interaction may be requested by the Principal, including deliverable walkthroughs and discussions related to the model framework, model inputs, model outputs, etc. The successful candidate will be expected to represent ISF with professionalism, positivity, and poise while describing technical concepts with simplicity and accuracy; the ability to communicate technical findings clearly to non-specialist audiences is vital to this role
• The successful candidate will be expected to be responsive, providing timely replies, proactively communicating blockers or schedule constraints, and comfort working within a government-contracted environment where deliverables carry external deadlines
• Doctorate preferred (Ph.D. or equivalent) in biostatistics, statistics, health data science, epidemiology, or a closely related quantitative field, combined with professional experience delivering quantitative and qualitative work in a client-facing or externally accountable context (e.g. a consulting firm, applied research organization, government advisory role)
• Experience working with or advising public sector or academic clients on quantitative and qualitative methodology, data systems, or analytical products, preferably in a public health or human services context
• AZ-204 certification is highly preferred
• Familiarity with version control (Git or equivalent) and the discipline of maintaining clean, reproducible, well-commented code. The ability to navigate and evaluate someone else's codebase is a meaningful part of the role. The ability to comply with the open science framework for reproducibility and traceability is preferred
• Proficiency in R, Python, or equivalent, including relevant packages for data manipulation, spatial analysis, and statistical modeling. Ability to work within RShiny and within an established Azure and Databricks environment following documented procedures is highly preferred
• Willingness to use Claude Code (Anthropic's AI coding assistant) as a productivity tool for reviewing scripts, running checks, and navigating the codebase. Prior experience with AI-assisted development tools is a plus

Benefits

• Independent Contractor – 1099
• Billed monthly based on hours worked
• Approximately 10–20 hours per month; as-needed basis; opportunity to grow into longer term engagement
• Available for occasional 1-hour meetings between 9 AM – 5 PM ET, particularly during onboarding

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