Senior Data Scientist, Wildfire Risk Scoring (Remote)

Cindera Inc.; Northern (KY); Hybrid; USD 120,000 - 180,000; Full time

Benefits offered by this job

Equity

Remote work

Job summary

Cindera Inc. is seeking a senior data scientist to translate wildfire hazard science into parcel-level readiness scores that are explainable and actionable for homeowners, insurers, and vendors.

You will work with data, product, and domain experts to define risk measurement, action-driven scoring, and transparent confidence assessments. The role involves end-to-end ownership of modeling problems, from data design and feature engineering through calibration and iteration as new evidence emerges.

Qualifications

• 5+ years of applied data science or ML in production.
• Experience with geospatial, property, or insurance data.
• Ability to explain modeling choices to non-technical partners.
• Experience shipping data products.
• Proficiency with data pipelines and calibration.

Responsibilities

• Build parcel-level readiness scores using hazard data, mitigation standards, and actions.
• Design feature sets and modeling approaches for exposure, vulnerability, and impact.
• Turn research outputs into production data pipelines and systems.
• Evaluate model performance, calibration, drift, and uncertainty.
• Encode wildfire science and mitigation logic transparently for customers.

Skills

Applied DS

Statistical modeling

Machine learning

Production ML

Geospatial data

Feature engineering

Model evaluation

Uncertainty

Explainability

Data pipelines

Education

MS in Data Science or related field

BS in Computer Science, Statistics, or related field

Tools

Python

SQL

Spark

GeoPandas

Git

Airflow

Job description

Cindera Inc. is seeking a senior data scientist to translate wildfire hazard science into parcel-level readiness scores that are explainable and actionable for homeowners, insurers, and vendors.

You will work with data, product, and domain experts to define risk measurement, action-driven scoring, and transparent confidence assessments. The role involves end-to-end ownership of modeling problems, from data design and feature engineering through calibration and iteration as new evidence emerges.