AI/ML Developer (Data Scientist)

Role Overview

Autoliv's primary goal is to Save More Lives. Our products never get a second chance. This is why we can never compromise on quality. We are working to increase vehicle safety by developing seatbelts, airbags and steering wheels and you can be part of our team as AI/ML Developer (Data Scientist).

In this role, you will be responsible for designing, developing, and deploying scalable Artificial Intelligence and Machine Learning solutions that create measurable business value across manufacturing, operations, quality, supply chain, and enterprise functions. You will work closely with business leaders and technical teams to transform data into actionable insights, predictive capabilities, and intelligent decision-making tools.

You will need to deliver production-ready AI solutions, drive user adoption, and ensure that machine learning initiatives generate tangible business outcomes, including productivity improvements, cost reduction, enhanced quality, and operational efficiency.

Responsibilities

Should you be interested in overseeing these tasks and aiming for enhanced performance standards, your role will involve:

• Partnering with business stakeholders to identify and prioritize high-value AI and Machine Learning opportunities.
• Translating complex business challenges into practical data science and AI solutions.
• Designing, developing, validating, and deploying machine learning models for business-critical applications.
• Building scalable AI solutions that integrate seamlessly with enterprise applications and workflows.
• Developing predictive, prescriptive, and optimization models to improve operational performance.
• Creating data-driven solutions for quality improvement, predictive maintenance, supply chain optimization, forecasting, and manufacturing analytics.
• Building reusable machine learning assets, frameworks, pipelines, and model components.
• Working with structured and unstructured datasets to develop robust analytical solutions.
• Deploying machine learning models using modern MLOps practices and cloud-based platforms.
• Integrating AI solutions through APIs, enterprise systems, dashboards, and business applications.
• Monitoring model performance, accuracy, drift, and business impact throughout the model lifecycle.
• Collaborating with Data Engineering teams to strengthen data pipelines and improve data quality.
• Partnering with IT, Cloud, Infrastructure, and Cybersecurity teams to ensure scalable, secure deployments.
• Creating visualizations and presenting findings in a clear and business-friendly manner.
• Promoting adoption of AI solutions by building trust, transparency, and stakeholder engagement.
• Driving continuous improvement of models, algorithms, and AI platforms.
• Contributing to enterprise AI standards, best practices, and data science governance.
• Supporting innovation initiatives by investigating emerging AI, ML, and Generative AI technologies.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, Engineering, Mathematics, or a related field.
• 6+ years of professional experience in Data Science, Machine Learning, Artificial Intelligence, or Advanced Analytics.
• Strong knowledge of Machine Learning algorithms including regression, classification, clustering, anomaly detection, and time-series forecasting.
• Experience with Deep Learning, Natural Language Processing (NLP), Computer Vision, or Generative AI technologies.
• Advanced programming skills in Python and hands-on experience with libraries such as NumPy, Pandas, Scikit-learn, TensorFlow, and PyTorch.
• Strong SQL skills and experience working with large-scale datasets.
• Experience using data visualization tools such as Power BI, Tableau, Matplotlib, or Seaborn.
• Knowledge of data engineering concepts, ETL pipelines, and data integration processes.
• Experience with big data technologies such as Spark, Hadoop, or similar frameworks.
• Hands-on experience with MLOps tools and practices including MLflow, Docker, Kubernetes, CI/CD pipelines, model monitoring, and lifecycle management.
• Experience deploying machine learning models into production environments.
• Exposure to cloud platforms such as Microsoft Azure and AI-related cloud services.
• Experience working with enterprise systems and integrating AI capabilities into operational workflows.
• Strong analytical thinking and the ability to solve ambiguous business problems.
• Excellent communication skills with the ability to explain technical concepts to non-technical audiences.