Data Scientist

INDEPENDENT CONTRACTOR (1099) POSITION

Quantitative Reviewer - Predictive Model & Analytics

Public Health Analytics

Engagement Type

Independent Contractor - 1099

Reports To

Principal, Management Consulting

Core Function

QA review of model outputs and documentation; primary backup support for lead data scientist

Education

Doctorate (Ph.D.) in biostatistics, statistics, health data science, epidemiology, or closely related quantitative field

Experience

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

Hours

Approximately 10-20 hours per month; as-needed basis; opportunity to grow into longer term engagement

Availability

Available for occasional 1-hour meetings between 9 AM - 5 PM ET, particularly during onboarding; Ability to meet internal deadlines as agreed with Principal.

Compensation

$80-$160/hour based on qualifications; billed monthly based on hours worked

Tools

Must be proficient in R, Azure, Azure Databricks, Claude Code

Data Access

Must currently hold, or have the ability to obtain, CITI certification to access restricted data

Project Overview

This contractor would support an applied public health research project producing a state-level decision-support system for a government agency client. The system is built on a two-stage predictive modeling pipeline: a Bayesian hierarchical abundance model that estimates the latent at-risk population from six surveillance outcomes across thousands of census tracts statewide, followed by a gradient-boosted machine learning layer with SHAP-based feature importance. Model outputs feed a live Azure-hosted interactive dashboard used by agency stakeholders for planning and resource allocation.

A lead data scientist with a doctorate in health data science currently manages the full analytical pipeline. A second data scientist supports data structuring and pipeline support. This Quantitative Reviewer would add independent quality assurance and advisory support, reviewing predictive model specifications, statistical outputs, intermediate calculations, and documented results, confirming the model and its outputs are correct, internally consistent, and accurately represented before it reaches the client. The Quantitative Reviewer would also provide primary backup for the lead data scientist as needed.

Responsibilities

Reviewing Model Specifications and Methods

• 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.

Verifying Analytical Outputs

• 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.

Checking Technical Documentation Accuracy

• 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.

Supporting Dashboard Validation

• 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.

Advising the Principal

• 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

Candidate Profile

Strong candidates will bring a doctoral credential alongside meaningful experience delivering technical work in externally accountable contexts, whether through consulting, applied research, government advisory work, or a combination. ISF values the ability to operate with professionalism and clarity in a client-service environment: translating complex findings for non-specialist audiences, managing your own time and scope reliably, and engaging with institutional clients. Experience advising or presenting to external clients, government agencies, or institutional stakeholders is vital to a successful candidacy.

Background & Experience

• 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.
• Comfort operating as an independent contractor within a structured engagement: estimating and tracking your own hours, meeting deadlines with appropriate autonomy, and communicating proactively with a manager when scope or schedule questions arise.

Bayesian & Spatial Statistics

• Solid working knowledge of Bayesian hierarchical modeling, including familiarity with MCMC estimation, estimating priors, convergence diagnostics (R-hat, ESS, trace plots), and posterior predictive checks. While the model framework has been developed, the successful candidate must have the ability to evaluate the model and its outputs.
• Familiarity with spatial random effects models, preferably with experience in geospatial modeling using tools such as ArcGIS and R packages inclusive of Conditional Autoregressive (CAR) or Intrinsic CAR (ICAR) structures used in disease mapping and small-area estimation. Ability to assess whether spatial borrowing has been implemented and validated appropriately.
• Understanding of abundance models or N-mixture frameworks that estimate latent populations from multiple partial observation processes, or equivalent experience with latent variable models in a public health context.

Quantitative Methods & Statistical Review

• Ability to verify that reported statistics (regression coefficients, credible intervals, cross-validated performance metrics, feature importance rankings) are correctly calculated and appropriately interpreted.
• Familiarity with penalized regression methods (elastic net, lasso) and cross-validation approaches, sufficient to evaluate whether a risk score construction methodology is sound.
• Comfort with gradient-boosted tree methods and SHAP-based feature importance at a conceptual and evaluative level.
• Strong numeracy: the ability to catch arithmetic errors, implausible values, and internal inconsistencies in tables of model results is a core requirement.

Public Health & Surveillance Context

• Sufficient familiarity with public health data and disease surveillance to assess whether model outputs (prevalence estimates, detection probabilities, county-level risk rankings) are epidemiologically plausible and appropriately caveated.
• Understanding of small-area estimation challenges, suppression and interval censoring in public health data, and the ecological inference limitations relevant to census tract-level models.
• Understanding of public health datasets is a plus.

Coding Proficiency & Data Organization

• 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.
• Strong experience with highly organized data storage practices and pipeline development. The project involves a structured Azure-based data environment with a layered medallion architecture; candidates should be comfortable working within and contributing to organized, well-documented data pipelines rather than ad hoc analytical workflows. 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.

Project Estimation & Time Management

• 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 Interface & Communication

• This role reports directly to the Principal. Technical collaboration with data scientists is expected; however, tasking, deadlines, and client engagement will be directed by the Principal.
• 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.

Engagement Logistics

IRB Compliance & CITI Training

Access to project data requires current CITI Program certification in Human Subjects Research. Candidates without current certification must complete CITI training (self-paced, available at citiprogram.org) before data access is granted. Additional data use agreement or IRB protocol requirements will be communicated at onboarding.

Independent Contractor Status

This is a 1099 independent contractor engagement. The contractor is responsible for their own taxes and benefits. No employment relationship is created or implied.

Confidentiality & Data Use

The contractor will have access to sensitive public health surveillance data governed by applicable data use agreements and confidentiality obligations

How to Apply

Please submit:

• Current CV or resume highlighting consulting and advisory experience alongside your quantitative training.
• A brief note (half page or less) describing your experience advising public sector or academic clients on quantitative work.

Applications reviewed on a rolling basis.

This independent contractor role as a Quantitative Reviewer focuses on providing crucial quality assurance and advisory support for an applied public health research project. You will be responsible for independently reviewing predictive model specifications, statistical outputs, intermediate calculations, and documented results to ensure accuracy, internal consistency, and appropriate representation of a state-level decision-support system for a government agency. Key responsibilities include verifying analytical outputs, checking technical documentation, supporting dashboard validation, and serving as primary backup for the lead data scientist within a complex predictive modeling pipeline that utilizes Bayesian hierarchical and gradient-boosted machine learning.

The ideal candidate will possess a Doctorate (Ph.D.) in biostatistics, statistics, health data science, epidemiology, or a closely related quantitative field, coupled with experience translating complex technical concepts for non-specialist audiences and advising public sector or academic clients. Essential technical skills include proficiency in R, Azure, Azure Databricks, and Claude Code, alongside a strong working knowledge of Bayesian and spatial statistics, quantitative methods, and public health data. This 1099 independent contractor position offers approximately 10-20 hours per month at $80-$160/hour, requires current CITI certification for data access, and demands excellent time management, proactive communication, and the ability to operate autonomously within a structured engagement.