Principal Data Scientist (Remote)

Principal Data Scientist

AF Group is seeking a Principal Data Scientist with expertise in either Commercial Property or Personal Homeowners insurance to serve as an individual contributor and technical authority on applying advanced analytics and machine learning to complex business problems, including pricing, risk selection, and other underwriting challenges. This role owns the end-to-end analytical lifecycle, from problem formulation and model development through deployment, monitoring, and governance. Partners closely with Actuarial, MLOps, and IT to deliver scalable, production-ready solutions. The Principal Data Scientist ensures long-term model performance through rigorous validation, drift monitoring, and audit-ready documentation, while advancing analytical best practices and evaluating emerging techniques relevant to commercial P&C insurance.

Responsibilities/Tasks:

• Acquires, organizes, and cleanses structured and unstructured data.
• Conducts in-depth analysis to uncover trends, risks, and business opportunities.
• Applies statistical modeling, machine learning, and advanced analytics to develop predictive and prescriptive solutions.
• Evaluates solution performance using statistically rigorous methods and measures the impact to business outcomes.
• Collaborates with MLOps and IT partners to transition solution prototypes from pilot validation into production environments.
• Ensures ongoing model health through post-deployment monitoring, drift detection, and audit-compliant governance practices.
• Creates and communicates results to senior level audiences of varying backgrounds, using business-facing presentations, reports, and dashboards.
• Authors and maintains comprehensive technical documentation for data lineage, codebases, results, and production changes.
• Provides technical and project guidance, including peer review of work, for data science team.
• Leads the evaluation of new analytic tools and processes.
• Drives investigation and adoption of advanced machine learning and AI innovations.

Education:

Bachelor's Degree in Data Science, Statistics, Mathematics, Operations Research, Actuarial Science, Computer Science, Engineering, Physics or related technical field required. Advanced degree preferred.

Experience:

10 years of experience in data science or related advanced analytics domains, including research and teaching, with 3+ years of technical leadership.

Required Skills/Knowledge/Abilities:

• 3+ years of experience supporting underwriting functions, including loss modeling, for Commercial Property (preferred) or Personal Homeowners insurance.
• Demonstrated expertise using Poisson, Gamma, and Tweedie distributions to build loss ratio, pure premium, and frequency–severity loss models for pricing.
• Extensive experience leveraging supervised learning models (e.g., XGBoost, GLM, etc.) and unsupervised techniques (e.g., K-means, PCA, etc.) to solve complex data science problems.
• Advanced Python programming skills supporting data science, including scikit-learn and pandas.
• Proficient data wrangling and ETL abilities using SQL on relational databases.
• Comfortable explaining machine learning models with partial dependence plots and SHAP values.
• Ability to conduct experiments e.g., A/B Testing, to evaluate the causal impact of model-driven decisions.
• Experience using version control tools such as Git and Azure DevOps.
• Experience working in cloud computing environments such as Azure, AWS, GCP, etc.

Preferred Skills/Knowledge/Abilities:

• Experience supporting at least one other commercial or personal line outside of Property lines.
• In-depth understanding of General Liability (aka Casualty), Workers Compensation, or Commercial Vehicle insurance.
• Knowledge of actuarial concepts and terminology used in pricing and ratemaking.
• Experience with Claims, Marketing, or Operations functions within P&C insurance settings.
• Ability to develop Agentic AI solutions to drive autonomous decision-making and task orchestration.
• Familiarity with causal modeling techniques such as Meta-learners, Causal Forest, Double ML, etc.
• Knowledge of advanced neural net architectures like LSTM, CNN, Transformers, Graph NN, etc.
• Understanding of NLP concepts such as topic modeling, Word2Vec, sentiment analysis, OCR, etc.
• Experience programming in the R language.
• Ability to build interactive dashboards using frameworks such as Plotly Dash, Power BI, Flask, etc.

Additional Information:

The above statements are intended to describe the general nature and level of work being performed by people assigned to this classification. They are not intended to be construed as an exhaustive list of all responsibilities, duties and skills required of personnel so classified. This job description does not constitute a contract for employment.

Pay Range:

Actual compensation decision relies on the consideration of internal equity, candidate's skills and professional experience, geographic location, market and other potential factors. It is not standard practice for an offer to be at or near the top of the range, and therefore a reasonable estimate for this role is between $137,900 and $231,000.

We are an Equal Opportunity Employer. We will not tolerate discrimination or harassment in any form. Candidates for the position stated above are hired on an "at will" basis. Nothing herein is intended to create a contract.