Skip to content
This role is no longer accepting applications via Rocketlist.

Explore similar active roles

O

Team Leader Ambasciatori

V

Technicien(ne) utilités / exploitation H/F

C

Associate General Counsel, Product Regulatory

C

Global Security Operations Center (GSOC) Operator

L

Product Coordination & Testing Team Leader

G

Production Designer

Tractian logo

Data Scientist - Predictive Maintenance

TractianPredictive Maintenance company
On-site
Sapphire Ventures logo
Sapphire Ventures
General Catalyst logo
General Catalyst
Next47 logo
Next47
NGP Capital logo
NGP Capital
Y Combinator logo
Y Combinator

About the role

  • Data Science at TRACTIAN The Data Science team at TRACTIAN focuses on extracting valuable insights from vast amounts of industrial data.
  • Using advanced statistical methods, algorithms, and data visualization techniques, this team transforms raw data into actionable intelligence that drives decision-making across engineering, product development, and operational strategies.
  • The team constantly works on optimizing prediction models, identifying trends, and providing data-driven solutions that directly enhance the company’s operational efficiency and the quality of its products.
  • What you'll do As a Data Scientist
  • Predictive Maintenance at TRACTIAN, you will work at the intersection of advanced data science and industrial operations.
  • Your mission is to develop cutting-edge algorithms and predictive models to monitor and predict equipment failures before they occur, optimizing asset reliability and reducing downtime.
  • You’ll face complex challenges involving large-scale time-series data, real-time data processing, and machine learning applications, while collaborating closely with engineers and laboratory teams to ensure our predictive maintenance solutions remain industry-leading.
  • Responsibilities Develop predictive maintenance algorithms using machine learning techniques for time-series data.
  • Analyze sensor data streams to identify patterns that predict equipment failure.
  • Research and stay up to date with academic literature and state-of-the-art condition monitoring techniques, translating relevant advances into practical solutions.
  • Collaborate with engineers to improve data pipelines and enhance model accuracy.
  • Build scalable, real-time models for low-latency predictions.
  • Create diagnostic tools that enable data-driven maintenance decisions.
  • Work with the laboratory team to design experiments and develop failure datasets using real machinery to validate hypotheses, develop new models, and optimize existing ones.
  • Continuously refine models based on real-world performance, experimental results, and feedback.
  • Requirements Expertise in machine learning, time-series analysis, and anomaly detection.
  • Proficiency in Python and common data science and ML libraries (e.g., NumPy, pandas, scikit-learn, PyTorch).
  • Solid understanding of signal processing concepts and hands-on experience with industrial sensor data (e.g., vibration, current, temperature, pressure).
  • Ability to read, interpret, and apply insights from academic literature and state-of-the-art research in condition monitoring and fault diagnosis.
  • Experience designing experiments to validate hypotheses and benchmark models.
  • Strong problem-solving skills and ability to handle noisy, high-dimensional data.
  • Advanced English.
  • Bonus Points Familiarity with both academic research and real-world applications in condition monitoring, fault diagnosis, and prognostics (e.g., vibration-based methods, model-based vs. data-driven approaches).
  • Experience translating academic methods into robust, production-ready algorithms.
  • Prior experience working in industrial or manufacturing environments.