Data Scientist

Innovation starts with being curious.

At Volvo Cars, we believe that being curious and truly committed to understanding people is the key to future success. We are people who care about other people, working together to create new technologies and innovations for safe, sustainable and convenient mobility. Want to join us?

 

Let's introduce ourselves

At Volvo Cars Safety Centre, we have around 200 highly skilled professionals who strive every day to make the world a safer place for vehicle occupants, their loved ones, and the people around them. Covering the Safety and Durability attributes, we relentlessly lead the way for safety in and support of the company’s strategic direction: Freedom to move in a personal, sustainable and safe way.

 

The Data, Analytics and Artificial Intelligence team at Volvo Cars Safety Centre works in large part with real world safety data to understand and explain accident and injury causation mechanisms and, thus, laying foundations for future safety features and functions in upcoming car development programs. We provide our knowledge to our car programmes through data analysis and knowledge needed to help drive the engineering of world leading products. The work is performed in close collaboration with Volvo Cars digital organisation and relevant data analytics organisations that provides us the governance and digital infrastructure.

We are currently looking for a data scientist with excellent project management skills. Your primary focus will be to lead and manage development of world class data analytics and AI products by applying scientifically established methods and techniques such as statistics, machine learning and deep learning. You shall achieve these by following best practices and well accepted design principles in software engineering and machine learning operations (ML/AIOPS). In addition, you may have the opportunity to be involved in developing novel methods in data science with automotive safety as a focus area.

 

What you'll do

Please note - this is not an Internship or recent graduate position!

Main responsibilities

• Lead the implementation of appropriate statistical, machine learning, and deep learning methodologies, ensuring alignment with business objectives, feasibility of execution, and scalability across different projects.
• Manage the development and deployment of data science solutions, coordinating cloud and on-prem infrastructure requirements while ensuring best practices in version control, reproducibility, and scalability.
• Oversee project timelines, resource allocation, and risk mitigation strategies, ensuring the successful execution of multiple data science initiatives while maintaining alignment with business priorities.
• Facilitate collaboration across cross-functional teams, including business stakeholders, engineering, and operations, to ensure transparency, adoption of best practices, and continuous improvement in data-driven decision-making.
• Ensure documentation, reporting, and knowledge sharing across projects, establishing governance frameworks, reproducibility standards, and compliance with ethical and regulatory requirements.

What you'll bring

Required qualifications

• University degree in a quantitative discipline (e.g., but not limited to – Mathematics, Computer Science, Data Science, Natural Sciences, etc.) or equivalent work experience,
• 3 + years previous work experience in writing high quality data science code in Python programming language. Proven track record and knowledge of best practices and standards in coding, familiarity with Python-based data processing and analysis libraries,
• Well versed with visualizing the insights in dashboards or similar solutions either off-the-shelf or Python-based frameworks,
• Significant experience in developing end-to-end Data Science software solutions,
• Experience with state-of-the-art data science and ML practices along with advanced knowledge of and experience with Python-based ML and DL libraries (particularly for ML) as well as version control (e.g., git and GitHub),
• Experience in developing scalable ML-based production systems in the cloud using multiple platforms such as Databricks and Azure,
• Familiarity and exposure to large lang