Industrial AI Data Scientist (Hybrid)

Etteplan; Tampere; Hybrid; EUR 56,000 - 67,000; Full time

Job summary

Etteplan seeks an experienced Data Scientist for Espoo or Tampere in a permanent hybrid role. You will translate industrial data into actionable insights, build predictive maintenance models, and define KPI-driven analyses aligned with customer projects.

Collaborate with cross-functional teams, adopt AI methodologies, and stay current with data science trends to deliver production-ready solutions in industrial digitalization.

Qualifications

• Proven experience as a Data Scientist with industrial use cases.
• Working experience about agentic workflows and tools.
• Experience with productizing models i.e., moving from notebook prototypes into a live production system.
• Experience with anomaly detection and predictive maintenance using sensor data.
• Proficiency in data profiling and visualization (e.g. Python, Power BI).
• Experience with data manipulation and analysis tools (e.g. SQL).
• Familiarity with cloud platforms like AWS, Azure, Google, etc.
• Excellent problem-solving skills and attention to detail.
• An applicable educational background.

Responsibilities

• Turn industrial data into meaningful insights and actionable decisions.
• Produce device failure and maintenance models as well as assess energy usage and usability facets.
• Design meaningful KPIs to support the business cases and facilitate data-driven decision making.
• Providing guidance and hands-on expertise on integrating AI methodologies into their business processes.
• Staying up to date with the latest developments in data science and agentic workflows.

Skills

Industrial data analysis

Problem solving

Anomaly detection

Predictive maintenance

Team collaboration

Education

Relevant degree

Tools

Python

Power BI

SQL

AWS

Azure

Google Cloud

Job description

Etteplan seeks an experienced Data Scientist for Espoo or Tampere in a permanent hybrid role. You will translate industrial data into actionable insights, build predictive maintenance models, and define KPI-driven analyses aligned with customer projects.

Collaborate with cross-functional teams, adopt AI methodologies, and stay current with data science trends to deliver production-ready solutions in industrial digitalization.