Experienced Health Insurance Actuary Needed — Evaluate New Environmental Risk Covariate for GLM

Experienced Health Insurance Actuary Needed — Evaluate New Environmental Risk Covariate for GLM Validation Description Beyond Intelligence is developing ATLAS®, a new environmental risk intelligence product designed for insurance modeling workflows. ATLAS converts complex environmental conditions into a standardized, deterministic Environmental Burden Score assigned to geographic locations. The purpose of ATLAS is to help insurers evaluate whether persistent environmental exposure differences explain variation in cost patterns, utilization, volatility, and risk across populations and locations. ATLAS is not a replacement actuarial model, pricing engine, AI prediction system, or individual risk score. It is designed as a model-ready external covariate that can be evaluated within existing actuarial workflows, including GLMs, forecasting models, reserving processes, and other risk analysis frameworks. We are looking for an experienced health insurance actuary or actuarial modeling expert to help us evaluate our validation methodology from the perspective of an insurance organization. Please Review Attached Materials Before Applying We included three brief attachments to provide context before submitting your proposal: 1. ATLAS Score Overview Document Explains: what the ATLAS Score measures how environmental complexity is transformed into a single 0–1 score why ATLAS is deterministic, explainable, and reproducible how it is intended to function as an actuarial covariate rather than a standalone prediction model 2. Global ATLAS Score Visualization Shows the broader concept: Environmental conditions across Earth → standardized geographic risk intelligence. The purpose is to demonstrate that ATLAS creates a consistent scoring framework across locations. 3. 100-Meter Resolution Visualization Shows how ATLAS can represent environmental burden differences across smaller geographic areas. This does not mean insurers must use address-level scoring. Resolution and aggregation can be adapted based on the actuarial use case (county, ZIP code, service area, portfolio, population-weighted regions, etc.). The key concept is that ATLAS creates a geographically anchored environmental variable that can be aggregated into existing modeling workflows. Initial Project Scope We are looking for actuarial guidance on: Reviewing our current validation methodology Identifying potential questions, objections, or concerns from insurer actuarial teams Improving alignment with real-world actuarial workflows Understanding how insurers evaluate new external datasets and model inputs Areas where your expertise may help: GLM integration medical cost modeling forecasting risk adjustment pricing/reserving workflows geographic aggregation methodology population weighting feature evaluation model validation governance considerations Our recommended validation approach is intentionally designed to minimize workflow disruption: An insurer compares their existing model against the same model with ATLAS added as an additional covariate, then evaluates whether ATLAS contributes incremental explanatory value, stability, or improved model performance. The goal is not to replace existing actuarial models. The goal is to determine whether environmental burden provides a useful additional signal inside existing models. Potential Long-Term Relationship For the right person, this may expand beyond the initial review. We are interested in developing a relationship with an actuarial advisor who could potentially support future insurer validation discussions, answer technical methodology questions, and help organizations evaluate ATLAS integration. Ideal experience: Health insurance actuarial modeling GLMs / predictive modeling Medical cost forecasting Pricing, reserving, or risk adjustment External data evaluation Model validation/governance ASA/FSA credentials are preferred but not required if you have strong practical modeling experience. When Applying, Please Briefly Answer: 1. Have you worked directly with health insurance actuarial models (GLMs, pricing, reserving, forecasting, risk adjustment, medical cost modeling, etc.) or evaluated external data/covariates? Please briefly describe your experience. 2. After reviewing the attached ATLAS materials, if a health insurer was evaluating this type of environmental covariate, what would you want to see during validation before considering it actuarially useful? What questions, concerns, or objections would you expect an actuarial team to raise? 3. If there is a strong fit, would you be interested in a longer-term advisory relationship supporting insurer validation discussions? We are looking for thoughtful actuarial expertise from someone who understands how new variables, datasets, and modeling approaches are evaluated inside real insurance organizations.