Job Description

Job Summary Minimum Requirements
Essential:

• Degree or Honours in Actuarial Science with a solid grounding in actuarial techniques and methodologies (e.g., risk modelling, claims prediction, survival analysis)
• 7+ years' experience in the insurance or financial services industry, with a focus on actuarial analysis and the application of machine learning to solve actuarial problems
• Proven expertise in building and deploying actuarial models (pricing, reserves, claims forecasting) combined with machine learning techniques to enhance decision-making
• Strong experience in claims modelling, survival analysis, and the application of predictive analytics to real-world actuarial problems
• Leadership experience in managing or mentoring a team of actuarial professionals and data scientists to deliver complex analytics solutions
• Proficiency in BI tools (e.g., Power BI, Tableau, Qlik Sense) for translating model outputs into business-friendly visualizations
• Excellent communication skills with the ability to convey complex technical findings in clear, actionable terms to non-technical stakeholders

Preferred:

• Hands-on experience with Python, R, SQL, and cloud platforms (e.g., Snowflake, AWS, or Azure) for model development and deployment
• Experience working with large datasets and building scalable data pipelines to support actuarial and machine learning models
• Ability to shape and prioritize both technical and business requirements, balancing actuarial rigor with business acumen
• Strong understanding of data-driven decision-making frameworks, especially in actuarial and insurance contexts

Expectations
Direct & Cultivate

• Lead, mentor, and develop a team of Data Scientists to deliver high-impact solutions using both actuarial methodologies and advanced machine learning techniques
• Cultivate a collaborative, high-performance culture that encourages innovative problem-solving and data-driven decision-making
• Ensure team members have the skills, resources, and support to push the boundaries of what's possible in actuarial and data science modelling

Design & Deliver

• Oversee the design, testing, and deployment of actuarial models (including pricing, claims reserving, and survival models) alongside machine learning models to solve critical business problems
• Combine actuarial science principles with advanced statistical modelling and machine learning algorithms to create highly effective, actionable solutions
• Measure, track, and communicate the commercial impact of models, ensuring clear business outcomes from every project
• Continuously evaluate and refine model accuracy, relevance, and alignment with shifting business priorities

Integrate Tech & Operations

• Act as the critical point of contact between the data scientists, and business stakeholders, ensuring alignment of business goals with technical deliverables
• Translate complex actuarial and machine learning concepts into clear, actionable insights for senior leadership, ensuring data-driven decisions are well understood across the organization
• Lead the integration of actuarial model outputs into the day-to-day operations, driving operational change and ensuring model adoption at all levels of the business

Enhance Performance

• Gather and document detailed requirements for actuarial analytics projects, ensuring both technical and business needs are understood and addressed
• Promote the use of best practices in actuarial modelling, data science techniques, and machine learning deployments, striving for continuous improvement and innovation
• Identify and implement opportunities for process improvements, ensuring the models are optimized and the business is maximizing the value of data

Salary Structure

• Negotiable Basic Salary
• Incentives
• Benefits

(Only suitable candidates will be shortlisted and contacted within 14 days)
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GAP Consulting
SA's Premium Recruitment Consultancy
GAP Consulting
2005
10-30
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