Senior Data Scientist | Hybrid Intelligence

Key Skills

Aartificial intelligence, Data Modeling, Data science techniques, Effective communication skills, Machine learning techniques, MLOps tools, Python Programming, R Programming

Job Description

We are Capgemini Engineering, part of the Capgemini Group.

Leaders in engineering services and R&D, with more than 65,000 people dedicated to Engineering and Science around the world. We help our clients accelerate their journey to smart industry, bringing global expertise and capabilities, cutting-edge digital technologies and software, agile engineering platforms and an industrialized execution model.

About the role

Our Hybrid Intelligence team at Capgemini Engineering is responsible for intelligently applying data science and machine learning technologies to deliver innovative and multidisciplinary solutions to our clients. We work for different sectors such as: Aerospace, Automotive, Pharma, Engineering, Industrial, Oil & Gas, among others. 

As part of this team you will work on a wide variety of innovative projects that make a difference, merging applied mathematics, scientific computing, Artificial Intelligence and software engineering.

Main Challenges

• Develop data-driven solutions to difficult business challenges across a wide range of business domains using state-of-the-art data science techniques and understand how to translate theory and models into business operations.
• Ensure the application of the data science solution to drive value and other business outcomes, as well as to support cost saving initiatives.
• Work collaboratively with different functional and engineering teams to design and implement end-to-end advanced analytics solutions, especially machine learning and artificial engineering, and monitor results.
• Lead the development of processes and tools to monitor and analyse the performance and accuracy of AI/ML systems (MLOps).
• Expert advice to the business in terms of opportunities, data modelling and data science.

You should have:

• Bachelor's, Master's or PhD degree (preferred) in Computer Science, Data Science, Statistics or related field.
• At least 5 years of experience as Data Scientist.
• Proficient in Python or R programming languages for data manipulation, analysis, and model development.
• Strong foundation in statistics and probability theory for hypothesis testing, regression analysis, and experimental design.
• Deep understanding of machine learning algorithms and techniques, including supervised learning, unsupervised learning, and neural networks.
• Skilled in data wrangling, including cleaning, preprocessing, and transforming raw data into usable formats for analysis and modelling.
• Fluency in English and Spanish (C1) and strong communication skills.

What we offer:

• Permanent contract.
• Flexible and Hybrid working model.
• 24 days holiday + 2 additional days + December 24th and 31st.
• Life and Accident Insurance.
• Restaurant ticket or meal subsidy.
• Flexible Compensation Plan (medical insurance, childcare, transport, training).
• Continuous training, you will be able to enjoy Mylearning and Capgemini University with access to platforms such as: Coursera, Udemy, Pluralsight, Harvard Manager Mentor, among others.
• Language training in English, French and German with Education First (EF).

Why should you join Capgemini Engineering?

• World leader in engineering.
• Relevant projects in innovation and R&D
• Part of a responsible company committed to equal opportunities.
• Multi-sector and multi-disciplinary expertise.
• Technology communities with a focus on continuous learning and improvement.
• Diverse, young-spirited teams that create a unique working environment!

Diversity is key to our strategy

Our commitment to inclusion and equal opportunities means that we have an Equality Plan and a Code of Ethics that guarantee the professional development of our staff and equal opportunities in their selection within an environment free of discrimination based on ethnicity, nationality, social origin, age, sexual orientation, gender expression, religion or any other personal, physical, or social circumstance.