Senior Expert Data Scientist MLOPs E1

• As Senior Expert Data Scientist (MLOPs) your main mission will be to implement Data Science Solutions to business challenges
  
• In particular your role as part of the MLOPs chapter will be to ensure that our Data Science models are easily deployable scalable monitored easily retrained and updated
  
• MLOPs operations are already mature and integrated into the larger Data architecture and CI/CD pipeline. Your role will be to increase this maturity and promote AI industrialization so that Data Solutions can be more efficient and more easily deployed across the company
  

• Implement and develop AI industrialization ensuring that all our Data Models are properly deployed on our MLOPs platform (Databricks) and in line with our Target Data architecture ensuring maximum efficiency of models
  
• Implement and manage sophisticated data science tools to ensure AI industrialization (models easily deployable scalable monitored retrained). Responsible for the implementation of our MLOPs and LLMOPs architecture
  
• Understand and peerreview data models developed to solve business challenges (incl. machine learning LLM GenAI). Finalize AI models prototypes developed by other Data Scientists
  
• Work with other departments (IT and business) to ensure operational use of results and measure impacts on key business indicators
  

• Work closely with business experts to understand the operational needs (actuarial marketing usage ) and translate business needs into mathematical models and problems
  
• Collect understand analyze integrate and explore the internal and external data to find correlations and relations
  
• Understand the business issues and implement data science following instructions
  
• Able to implement a Feature Engineering on him/her own
  

• Active communication and support with relevant departments
  
• Cooperation with various teams (business IT Architecture Data Teams)
  
• Cooperation with other Group companies to participate to the improvement of Group AI maturity
  

• MLOps
  
• MLOps
  
• MLOps CI/CD
  
• AI
  

• AI MLOPs Databricks
  
• AI MLOPs LLMOPs
  
• LLM GenAI AI
  
• IT
  

• 
  
• IT
  
• AI
  

Requirements

• Strong scientific education (Master s degree or PhD)
  
• Ability to model the reality of business into equations and to understand the implications and limitations of a model
  
• Programming skills (ideally Python or similar; appreciated: R Java/ Scala)
  
• Strong 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. scikitlearn 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)
  

• Autonomy
  
• Academic curiosity / passion for empirical research and for answering hard questions with data
  
• Customer oriented
  
• Results focused
  
• Continuous learner
  
• Cooperation
  
• English:fluent in speaking and writing Business level English is required
  

• 
  
• 
  
• Python R Java/Scala
  
• 
  
• SQL Pig/Hive
  
• : scikitlearn Spark MLlib R
  
• : Spark
  

• 
  
• /
  
• 
  
• 
  
• :
  

• As Senior Expert Data Scientist (MLOPs), your main mission will be to implement Data Science Solutions to business challenges
  
• In particular, your role as part of the MLOPs chapter will be to ensure that our Data Science models are easily deployable, scalable, monitored, easily re-trained and updated
  
• MLOPs operations are already mature and integrated into the larger Data architecture and CI/CD pipeline. Your role will be to increase this maturity and promote AI industrialization, so that Data Solutions can be more efficient and more easily deployed across the company
  

• Implement and develop AI industrialization, ensuring that all our Data Models are properly deployed on our MLOPs platform (Databricks) and in line with our Target Data architecture, ensuring maximum efficiency of models
  
• Implement and manage sophisticated data science tools to ensure AI industrialization (models easily deployable, scalable, monitored, retrained). Responsible for the implementation of our MLOPs and LLMOPs architecture
  
• Understand and peer-review data models developed to solve business challenges (incl. machine learning, LLM, GenAI). Finalize AI models prototypes developed by other Data Scientists
  
• Work with other departments (IT and business) to ensure operational use of results and measure impacts on key business indicators
  

• Work closely with business experts to understand the operational needs (actuarial, marketing, usage ) and translate business needs into mathematical models and problems
  
• Collect, understand, analyze, integrate and explore the internal and external data to find correlations and relations
  
• Understand the business issues and implement data science following instructions
  
• Able to implement a Feature Engineering on him/her own
  

• Active communication and support with relevant departments
  
• Cooperation with various teams (business, IT, Architecture, Data Teams)
  
• Cooperation with other Group companies to participate to the improvement of Group AI maturity
  

• MLOps
  
• MLOps
  
• MLOps CI/CD
  
• AI
  

• AI MLOPs Databricks
  
• AI MLOPs LLMOPs
  
• LLM GenAI AI
  
• IT
  

• 
  
• IT
  
• AI
  

Requirements

• Strong scientific education (Master s degree or PhD)
  
• Ability to model the reality of business into equations, and to understand the implications and limitations of a model
  
• Programming skills (ideally Python or similar; appreciated: R, Java/ Scala)
  
• Strong 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)
  

• Autonomy
  
• Academic curiosity / passion for empirical research and for answering hard questions with data
  
• Customer oriented
  
• Results focused
  
• Continuous learner
  
• Cooperation
  
• English:fluent in speaking and writing Business level English is required
  

• 
  
• 
  
• Python R Java/Scala
  
• 
  
• SQL Pig/Hive
  
• : scikit-learn Spark MLlib R
  
• : Spark
  

• 
  
• /
  
• 
  
• 
  
• : -