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Actuarial Business Analyst (Data Science and Machine Learning Focused)
Zusammenfassung
A leading recruitment agency is seeking an experienced Actuarial Business Analyst to lead a team of Data Scientists in developing Machine Learning models. The role demands a strong background in the Insurance sector, with at least 7 years' experience, and an Honours / Degree in Actuarial Science. Responsibilities include overseeing model development, engaging with stakeholders, and ensuring the operational adoption of insights. Proficiency in BI tools such as Power BI or Tableau is required, along with leadership experience.
Qualifikationen
- Minimum 7 years’ experience in the Insurance or Financial Services sector.
- Proven experience in developing statistical and/or Machine Learning models.
- Demonstrated ability to assess and articulate the business impact of Machine Learning models.
Aufgaben
- Lead and mentor a team of Data Scientists to deliver impactful Machine Learning solutions.
- Oversee the development, testing, and deployment of Machine Learning models.
- Serve as the primary liaison between the Data Science team and business stakeholders.
Kenntnisse
Ausbildung
Tools
ENVIRONMENT : OUR client is redefining how insurance and technology work together and is looking for an experienced Actuarial Business Analyst with a strong focus on leading a team of Data Scientists to design, develop, and implement Machine Learning models. The role involves evaluating and quantifying the business impact of these models, translating complex analytical outputs into clear, actionable insights, and ensuring operational teams fully understand and leverage the results. You will act as the key bridge between Data Science and business, driving measurable value through advanced analytics. The successful incumbent must possess an Honours / Degree in Actuarial Science with 7 years’ experience in the Insurance or Financial Services sector (preferably short-term insurance) including proven experience in developing statistical and / or Machine Learning models. You also require proficiency in BI tools such as Power BI, Qlik Sense, Tableau, or similar platforms.
DUTIES :
- Lead and mentor a team of Data Scientists to deliver impactful Machine Learning solutions.
- Foster a culture of innovation, collaboration, and continuous improvement.
- Oversee the development, testing, and deployment of Machine Learning models.
- Evaluate and quantify the business impact of analytical solutions.
- Ensure models are accurate, relevant, and fit-for-purpose.
- Serve as the primary liaison between the Data Science team and business stakeholders.
- Translate technical concepts into actionable recommendations for both technical and non-technical audiences.
- Communicate insights effectively, ensuring operational adoption of model outputs.
- Gather, define, and document business and technical requirements for Data Science projects.
- Ensure projects align with strategic business objectives and deliver measurable outcomes.
- Champion best practices in analytics, modelling, and business integration.
- Identify opportunities to improve modelling approaches and operational use of analytics.
- Degree or Honours in Actuarial Science.
- Minimum 7 years’ experience in the Insurance or Financial Services sector (preferably short-term insurance).
- Proven experience in developing statistical and / or Machine Learning models.
- Demonstrated ability to assess and articulate the business impact of Machine Learning models.
- Previous leadership experience within Data Science, Analytics, or Actuarial teams.
- Experience with Claims Modelling and Survival Analysis / Modelling in the Insurance industry.
- Proficiency in BI tools such as Power BI, Qlik Sense, Tableau, or similar platforms.
- Familiarity with Python, R, SQL, and cloud data platforms such as Snowflake.
- Understanding of data-driven decision-making frameworks.
* Der Gehaltsbenchmark wird auf Basis der Zielgehälter bei führenden Unternehmen in der jeweiligen Branche ermittelt und dient Premium-Nutzer:innen als Richtlinie zur Bewertung offener Positionen und als Orientierungshilfe bei Gehaltsverhandlungen. Der Gehaltsbenchmark wird nicht direkt vom Unternehmen angegeben. Er kann deutlich über bzw. unter diesem Wert liegen.