Associate Actuary

Key Skills

Data Analytics, Data Engineering, Data science techniques, Predictive Modeling, Python Programming, R Programming, SAS

Job Description

The company is an equal opportunity employer.

Responsibilities

Key Risk’s Actuarial/Business Intelligence and Analysis team transforms raw data into actionable business solutions. They leverage their in-depth industry knowledge to draw relevant insights and utilize statistical models and algorithms to deliver strategies for effective data analysis and to facilitate communications of these results to our organization’s decision makers. The team is aligned into three core pillars: (1) Pricing/Profitability/Performance Analytics, (2) Data Science/Predictive Modeling, and (3) Reserving/Budgeting.

The Associate Actuary is responsible for developing, reviewing, and communicating actuarial analyses and models in collaboration with assigned associates and various teams across the WR Berkley enterprise. These analyses support the estimation of expected losses across the insurance portfolio and the projection of future financial outcomes. This role specifically will support pricing, profitability tracking, and predictive modeling/analytics for Key Risk.

To enhance the department’s contribution to the company’s success, the Associate Actuary is also expected to proactively identify, recommend, and communicate improvements to actuarial processes and analytical methods in response to evolving market and operational conditions.

The Associate Actuary will report directly to the Chief Actuary in a leadership role for analytics at Key Risk.

Key Functions will include but not be limited to:

• Manage staff in the Actuarial/BI&A team directly as well as other associates indirectly, depending on the project;
• Engage regularly with senior management teams at the strategic level to execute on goals;
• Collaborate across the operating functions at Key Risk and within the mono-line WC segment to implement process efficiencies through automation and predictive analytics;
• Maintain a leadership role in project management and project execution on Actuarial and BI&A related undertakings;
• Create and deliver presentation to internal stakeholders as needed;
• Provide subject matter expertise for the Data Science and Analytics Data Engineering team to effectively enhance our data analytics data stores;
• Pursue ongoing professional development through actively maintaining eligibility in the Actuarial Study Program, continuing education, advanced accreditation in the insurance and analytics fields, and participating on professional advisory boards and committees;
• Pricing/Profitability/Performance Analytics-Accountable for timeliness and quality of Actuarial aspects of product development, pricing, profitability analyses.
• Data Science/Predictive Modeling-Accountable for the timely execution of high-value data science projects.

Qualifications

Education Requirements:

• CAS membership or near attainment of the ACAS credential is preferred. Pursuit of ACAS and/or FCAS designation required;
• Bachelor’s or advanced degree in mathematics, statistics, actuarial science, or other similar quantitative field (or college degree with significant technical experience in a corporate setting).

Qualifications:

Knowledge & Experience

• A minimum of 5 years of actuarial experience, preferably with extensive background in pricing and data science;
• Advanced knowledge of actuarial ratemaking methodologies;
• Intermediate to advanced knowledge of predictive analytics techniques and model deployment;
• Proficiency working with relational databases (e.g., SQL Server), unstructured data and statistical software (e.g., R, Python, SAS) to support analytical objectives;

Personal Attributes

• Excellent listening and interpersonal skills;
• Customer-focused mindset;
• Commitment to the highest standards of ethical behavior, honesty, and integrity.

Additional Company Details

We do not accept any unsolicited resumes from external recruiting agencies or firms.

The actual salary for this position will be determined by a number of factors, including the scope, complexity and location of the role; the skills, education, training, credentials and experience of the candidate; and other conditions of employment.