Data Scientist

Position Title: Data Scientist
Capgemini Engineering is a global leader in engineering services, bringing together a worldwide team of engineers, scientists, and architects to assist the most innovative companies in unleashing their potential.

Position Overview: We are seeking a skilled Data Scientist with expertise in Cognite Data Fusion, data modelling, Unified Namespace (UNS), ontologies, and the identification of data products and datasets. The ideal candidate will have a strong background in developing and implementing data science projects, analyzing large and complex data sets, and driving data-driven decision-making across the organization

Design, implement, and deploy scalable data solutions utilizing Cognite Data Fusion, focusing on data modeling, UNS, and ontologies to address industry-specific challenges.​
Data Analysis: Analyze large and complex data sets to identify trends, insights, and opportunities, supporting solution development and business strategies.​
Collaboration: Collaborate with cross-functional teams to understand data needs and translate them into data science solutions, ensuring seamless integration and operationalization of digital solutions across various domains.
Client Engagement: Engage with clients to understand their business objectives, lead discovery workshops, and provide expert guidance on data-driven strategies and potential challenges.​
Develop dashboards and visualizations using tools such as Power BI, Grafana, or web development frameworks like Plotly Dash and Streamlit to effectively communicate data insights.​
Mentorship: Provide guidance and mentorship to junior team members, promoting best practices in data science and software development.​

Educational Background: Master’s or PhD degree in a quantitative field.​
Experience: Minimum of 2 years of experience in data science, with a strong background in developing analytical solutions within domains such as pharma, oil and gas, manufacturing, or power & utilities.​
Technical Skills: Proficiency in Python and its data ecosystem (pandas, numpy), machine learning libraries (scikit-learn, keras), and experience with SQL.​
Visualization Tools: Experience with data visualization tools like Power BI, Grafana, Tableau, or web development frameworks such as Plotly Dash and Streamlit.​
Software Practices: Strong understanding of software development practices, including version control (e.g., Git), automated testing, and documentation.​
Cloud Platforms: Experience with cloud services such as GCP, Azure, or AWS is advantageous.​
Domain Knowledge: Familiarity with industrial data management concepts, including Unified Namespace (UNS), ontologies, and data product identification.​
Demonstrated ability to lead projects and mentor junior team members.​

Experience serving as a domain expert on internal or customer projects within relevant industries.​
Cloud Deployment: Experience deploying models and solutions in production environments using cloud infrastructure.​
Continuous Learning: Willingness to stay updated with the latest developments in data science and related technologies.​

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