Supervisor, Actuarial Predictive Modeling

AmTrust Financial Services, Inc.; United States; On-site; USD 125,000 - 165,000; Full time

Job summary

AmTrust Financial Services, Inc. is seeking an experienced actuarial candidate to join their Actuarial Predictive Modeling team. This supervisory role requires collaboration with underwriting and data science teams to build and enhance complex pricing models.

The ideal candidate will have a Bachelor's degree in Actuarial Science, 5+ years of relevant experience, and strong leadership skills. You will be responsible for guiding junior staff, ensuring model validation, and fostering a risk management culture.

A diverse and innovative work environment awaits you, where your ability to convey complex ideas effectively will greatly contribute to the team's success.

Qualifications

• 5+ years of actuarial pricing experience, ideally in small commercial insurance.
• Proven ability to manage and mentor a team.
• Ability to communicate technical concepts to non-technical audiences.

Responsibilities

• Collaborate with teams to develop predictive models.
• Validate models and establish usage guardrails.
• Drive decisions around model rollout and monitoring.

Skills

Data exploration skills

SQL proficiency

R or Python experience

Statistical analysis

Machine learning technologies

Education

Bachelor's degree in Actuarial Science or related field

ACAS or FCAS designation

Tools

Git

Job description

Overview

We are seeking a driven, inventive, and thoughtful actuarial candidate to guide the adoption of complex models across multiple lines of business. This role will be a part of our Actuarial Predictive Modeling team, which is responsible for ensuring that pricing models are built with actuarial principles in mind. The role involves collaborating with underwriting management, data scientists, and other actuarial teams to build pricing models and evaluate model usage initiatives. More specifically, the candidate will be expected to propose model experimentation ideas during development, recommend expected loss adjustments and guardrails for the final trained models, communicate the benefits and concerns of various technical modeling choices, and influence management decisions related to predictive model usage.

This is a supervisory position overseeing one or more members of the Actuarial Predictive Modeling team, with a strong emphasis on people management and team leadership. The candidate will be expected to have a high level of proficiency and knowledge in both actuarial and modeling areas, as well as a genuine interest in developing others. The candidate will guide staff members in both everyday project work and long-term professional development. Effective communication with both technical and non-technical audiences is crucial, as is the ability to understand business priorities, translate them into technical requirements, and manage resources efficiently. The ideal candidate will be able to think quickly on their feet in response to management queries. Additionally, the candidate will own actuarial documentation, play an active role in model filings, and help drive continuous improvements in team workflows.

Responsibilities

• Collaborate with various teams to understand, develop, and implement predictive models for pricing, portfolio management, and planning purposes
• Ensure appropriate data adjustments and assumptions are applied during model development
• Challenge the model and communicate their strengths and weaknesses to stakeholders
• Validate models for their intended purpose and establish usage guardrails
• Drive decisions and alignment around model rollout, monitoring, and addressing potential misuse
• Provide timely support for model filings as needed
• Maintain positive collaboration with business and stakeholders, promptly resolve inquiries and requests, and communicate effectively to ensure transparency and common understanding of goals and prioritization
• Provide input on cross‑departmental steering committees for strategic projects
• Maintain an up‑to‑date understanding of the insurance landscape, emerging trends, data science concepts, and new technologies impacting pricing models
• Foster a strong risk management culture by maintaining model documentation, managing change management processes, and adhering to established model governance policies and best practices for data and code management
• As a supervisor, work closely with junior staff to oversee the development of professional skills, elevate technical and actuarial knowledge, and guide projects to successful and timely completion
• Develop and enhance team processes to be effective, repeatable, and scalable
• Serve as an actuarial modeling expert by providing technical model support and relevant training to other actuaries and underwriters within the organization

Qualifications

• Bachelor's degree in Actuarial Science, Mathematics, or a related field
• 5+ years of actuarial pricing experience, ideally in small commercial insurance
• ACAS or FCAS designation and in good standing with the Casualty Actuarial Society
• Strong data exploration and manipulation skills, ideally in SQL, R, or Python
• Experience working with Git
• Solid understanding of statistical methods applied to insurance data, with the ability to identify and articulate data and modeling issues
• Experience with predictive models for Pricing including understanding of assumptions, interpretation of results, and standard model evaluation techniques
• Experience with modern machine learning technologies such as GBM
• Experience managing, supervising, or mentoring team members
• Proven ability to build consensus and drive large‑scale projects to conclusion
• Ability to build a sense of trust and rapport that creates an effective workplace
• Excellent problem‑solving skills and attention to detail
• Demonstrated ability to experiment with and learn new technologies

The expected salary range for this role is $125K-$165K/year.

Please note that the salary information shown above is a general guideline only. Salaries are based upon a wide range of factors considered in making the compensation decision, including, but not limited to, candidate skills, experience, education and training, the scope and responsibilities of the role, as well as market and business considerations.