(Senior) Data Science Consultant (Data Scientist)

Key Skills

Java Programming, Finance, Design, Python Programming, Framework, SQL, Agile methodologies, Git, Big Data Technology, Machine learning techniques, Data science techniques, Data Mining, Deep Learning, Natural Language Processing (NLP), Continuous Integration & Continuous Delivery - CI/CD, Algorithms, SPARK Programming, Databricks, Data Analysis

Job Description

Capgemini Invent is the global consulting unit of the Capgemini Group for Digital Innovation and Transformation. It helps CxOs plan and shape the future of their businesses. The team combines strategy, technology, data science and creative design with in-depth industry expertise to develop new digital solutions and the business models of the future. Capgemini Invent employs more than 7,000 people at more than 30 locations and 25 creative studios worldwide

We are a rapidly growing team of talented Data Science enthusiasts which have dedicated themselves to building innovative and data-driven products & services for customers across industries. Our aim is to rapidly develop cutting-edge solutions by applying the latest technologies and data science techniques.

What do you need to start?

A data science enthusiast who is energetic and curious, who will help us discover the information hidden in vast amounts of data, and help stakeholders make smarter, more data-driven decisions and boost their businesses. Primary focus will be in applying data science techniques, statistical analysis, and building high quality machine learning algorithms.

In a nutshell you should have:

• Team-player attitude with extensive communications skills (English is a must, German is a plus) appreciating international client-facing activities (travelling across Europe might be required)
• At least 3 years of experience in the data science domain
• Grounded working knowledge of statistical methods and their implementation (including Deep Learning and/or Natural Language Processing)
• Experience in programming languages such as Python/R (front-end is a plus)
• Experience working agile with Git, CI/CD, multi-services
• Experience with building and deploying machine learning models
• Experience with cloud computing platforms, preferably Azure
• Expertise in at least one business domain (banking, finance, automotive, manufacturing, life science, etc.)
• BI experience is a plus

What duties will help you grow?

As an integral part of global Data Science community within Capgemini Invent and Capgemini Group, you will help our Clients towards transformations using AI / Data Science or automation technology to at the very forefront of innovation in different sectors. You will have the opportunity to shape the future of business operations, build new models or solutions including prototyping. We seek focused and inspired individuals who have the special power to build the close, trusting relationships that are key to delivering the seamless and efficient transitions that Clients expect from Capgemini.

• Develop end-to-end data science solutions using machine learning frameworks and big data technologies (e.g. Python/R/Databricks/Spark)
• Work as a team member or leader in data science projects across various industries and sectors
• Participate in initiatives and sales offerings in the data science field
• Feature selection, model building and optimizing classifiers / regressors, etc. using machine learning techniques. Develop Models using open source packages e.g. Python and R.
• Prepare workshop for requirement gathering and developing solutions.
• Work to design and develop analytical/ data mining/ machine learning/deep learning models as part of data science solutions.
• Design and build an end-to-end prototype data science solution to a business problem in any specific sector/ function, more specific to any manufacturing and asset intensive industry domains.
• Process, cleans, and verify the integrity of data used for analysis.
• Use state-of-the-art methods for data mining and performing EDAs.
• Help enhance data collection procedures to include information that is relevant for building analytical systems.
• Doing ad-hoc analysis and presenting results in a clear manner.