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About This Role
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
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- 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
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- 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
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- 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
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- 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
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- 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
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- 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
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IRB Compliance \& CITI Training
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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.
Salary Context
This $156K-$332K range is above the 75th percentile for Data Scientist roles in our dataset (median: $155K across 226 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At ISF, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 463 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($244K) sits 27% above the category median. Disclosed range: $156K to $332K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
ISF AI Hiring
ISF has 1 open AI role right now. They're hiring across Data Scientist. Based in Remote, US. Compensation range: $332K - $332K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
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