Data Scientist - Temporary

ABB
Data Scientist - Temporary
At ABB, we are dedicated to addressing global challenges. Our core values: care, courage, curiosity, and collaboration - combined with a focus on diversity, inclusion, and equal opportunities - are key drivers in our aim to empower everyone to create sustainable solutions. Write the next chapter of your ABB story.
This position reports to
Global Quality Manager
Your role and responsibilities
In this role, you will have the opportunity to work closely with our experienced data scientists and engineers to support the development of innovative solutions in the areas computer vision for anomalies detection, predictive maintenance, and generative AI. The work model for the role is: #LI-Onsite This role is contributing to the Smart Power business in Frosinone. You will be mainly accountable for: • Conducting research and analysis on the latest data science techniques and methodologies • Developing AI solutions for aesthetic anomaly detection for industrial quality assurance, predictive maintenance, and advanced LLM-based chatbots working with internal company data • Cleaning and preprocessing large and complex data sets • Collaborating with engineering teams to integrate models and algorithms into our existing systems • Evaluating and interpreting model results and providing actionable insights and recommendations • Communicating findings and recommendations to stakeholders in clear and concise ways You will join a dynamic team
Qualifications for the role
More about us
We value people from different backgrounds. Apply today for your next career step within ABB and visit www.abb.com to learn about the impact of our solutions across the globe. #MyABBStory For more information, please, contact the Talent Partner - Chiara Mazzoleni.
At ABB, we are dedicated to addressing global challenges. Our core values: care, courage, curiosity, and collaboration - combined with a focus on diversity, inclusion, and equal opportunities - are key drivers in our aim to empower everyone to create sustainable solutions. Write the next chapter of your ABB story.
This position reports to
Global Quality Manager
Your role and responsibilities
In this role, you will have the opportunity to work closely with our experienced data scientists and engineers to support the development of innovative solutions in the areas computer vision for anomalies detection, predictive maintenance, and generative AI. The work model for the role is: #LI-Onsite This role is contributing to the Smart Power business in Frosinone. You will be mainly accountable for: • Conducting research and analysis on the latest data science techniques and methodologies • Developing AI solutions for aesthetic anomaly detection for industrial quality assurance, predictive maintenance, and advanced LLM-based chatbots working with internal company data • Cleaning and preprocessing large and complex data sets • Collaborating with engineering teams to integrate models and algorithms into our existing systems • Evaluating and interpreting model results and providing actionable insights and recommendations • Communicating findings and recommendations to stakeholders in clear and concise ways You will join a dynamic team
Qualifications for the role
- You hold a degree in Data Science or related field.
- Knowledge of machine learning, deep learning, and artificial intelligence principles and algorithms.
- Knowledge of Python, SQL and other data science tools and libraries.
- Familiarity with computer vision techniques and technologies.
- Good written and verbal communication skills in English.
- Strong analytical and problem-solving skills.
- Ability to work collaboratively in a team environment
More about us
We value people from different backgrounds. Apply today for your next career step within ABB and visit www.abb.com to learn about the impact of our solutions across the globe. #MyABBStory For more information, please, contact the Talent Partner - Chiara Mazzoleni.
JOB SUMMARY
Data Scientist - Temporary
ABB

Frosinone
3 days ago
N/A
Full-time