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) 
Please send your CV to [email protected]
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GAP Consulting
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