Job Purpose
As Lead Data Scientist, your main mission will be to work with business and other Data teams to conceive, develop and implement Data Models (Machine Learning, GenAI) to provide actual solutions to business challenges. The company has the ambition to use Data to improve business efficiency in various areas (marketing, sales, customer journey, claims, contract management, etc…). You will be part of the effort to provide advanced data solutions to such business challenges.
Key Responsibilities
Data Analysis
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Work closely with business experts to understand the operational needs (actuarial, marketing, distribution, claims, customer services, customer journey, …) and translate business needs into mathematical models and problems.
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Collect, understand, analyze, integrate and explore the internal and external data to find correlations and relations (large quantity of data available on Datalake for such analysis).
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Understand the business issues and develop data science models (AI, GenAI) following instructions and peer reviews.
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Able to lead Exploratory Data Analysis (with support).
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Able to implement a Feature Engineering on him/her own.
Design and Propose
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Provide a proposal to solve an issue on the basis of the result of data analysis.
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Apply sophisticated data science tools to solve business challenges (incl. machine learning, GenAI)
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Work with other departments (IT and business) to ensure operational use of results and measure impacts on key business indicators. Regularly monitor and re-train implemented models to ensure maximum efficiency.
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Responsible for proposal of the design, implementation, evaluation and completion of PDCA on the basis of the result of data analytics
Communication
Required Skills and Experience
Technical and Professional Skills
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Strong scientific education (Master's degree or PhD)
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Ability to model the reality of business into equations, and to understand the implications and limitations of a model
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Programming skills (ideally Python or similar; appreciated: R, Java/ Scala)
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Strong experience in data processing (e.g. data mining, social network analysis, text mining…)
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Practice with database access (SQL; optionally Pig and Hive)
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Knowledge and practice of machine learning, including libraries (e.g. scikit-learn, Spark MLlib, R packages)
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Knowledge and practice of AI Industrialization (MLOPs, LLMOPs); ideally, experience on Databricks (if not, training available)
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Appreciated: practice with a modern distributed computing framework (like Spark)
Soft Skills and Competencies