Data Scientist

As a 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 practical solutions to business challenges.

Data Analysis:

• Work closely with business experts to understand operational needs (actuarial, marketing, distribution, claims, customer services, customer journey, etc.) and translate business needs into mathematical models and problems
• Collect, understand, analyze, integrate, and explore internal and external data to identify correlations and relationships, utilizing large quantities of data available on the Datalake
• Understand business issues and develop data science models (AI, GenAI) following instructions and peer reviews
• Lead Exploratory Data Analysis (with support)
• Implement Feature Engineering independently

Design and Propose:

• Provide proposals to solve issues based on data analysis results
• Apply advanced data science tools to solve business challenges, including machine learning and GenAI
• Collaborate with other departments (IT and business) to ensure the operational use of results and measure impacts on key business indicators
• Regularly monitor and re-train implemented models to ensure maximum efficiency
• Responsible for proposing the design, implementation, evaluation, and completion of the PDCA cycle based on data analytics results
• Engage in active communication and support with relevant departments

Technical and Professional Skills:

• Ability to model business realities into equations and understand the implications and limitations of a model
• Programming skills (ideally Python or similar; appreciated: R, Java/Scala)
• Extensive experience in data processing (e.g., data mining, social network analysis, text mining)
• Practice with database access (SQL; optionally Pig and Hive)
• Knowledge and practice of machine learning, including libraries (e.g., scikit-learn, Spark MLlib, R packages)
• Knowledge and practice of AI Industrialization (MLOps, LLMOps); ideally, experience on Databricks (if not, training available)
• Appreciated: practice with a modern distributed computing framework (like Spark)

Soft Skills and Competencies:

• Autonomy , Continuous learner , Cooperative
• Academic curiosity/passion for empirical research and solving complex problems with data
• Customer-oriented , Results-focused