Data Scientist Predictive Maintenance (f/m/d)
In our mission to become the first industrial tech company in the sector, we decided to bundle Data Analytics, Data Science, AI and Hyperautomation (RPA)
within our new team “Data & Insights”. We build a rock-solid foundation by putting in place the processes, organizational structures, technical infrastructure, training, and enablement to support broad adoption of our solutions. We will pave new ground to transform Heidelberg Materials to a modern data driven company.
As a Data Scientist Predictive Maintenance, you will report to the Senior Manager AI and MLOps. You will be part of the product team “HProduce Maintain” and work collaboratively with other Data Scientists and Software Engineers on the Kiln Monitoring System Software, improving, developing and implementing anomaly detection. In this role, you will have the chance to stay up-to-date with the latest machine learning technologies and create impactful solutions that help us prevent (kiln) production stoppages. Hence, with your data science models, you will contribute to reduce costs and help us meet our sustainability targets.
You will be part of a diverse and international team that values gender equity and welcomes individuals from all backgrounds and identities.
Preferred Qualifications:
We are convinced that only those who successfully realize their personal goals can also fully contribute professionally. That's why we offer you attractive benefits, such as:
within our new team “Data & Insights”. We build a rock-solid foundation by putting in place the processes, organizational structures, technical infrastructure, training, and enablement to support broad adoption of our solutions. We will pave new ground to transform Heidelberg Materials to a modern data driven company.
As a Data Scientist Predictive Maintenance, you will report to the Senior Manager AI and MLOps. You will be part of the product team “HProduce Maintain” and work collaboratively with other Data Scientists and Software Engineers on the Kiln Monitoring System Software, improving, developing and implementing anomaly detection. In this role, you will have the chance to stay up-to-date with the latest machine learning technologies and create impactful solutions that help us prevent (kiln) production stoppages. Hence, with your data science models, you will contribute to reduce costs and help us meet our sustainability targets.
You will be part of a diverse and international team that values gender equity and welcomes individuals from all backgrounds and identities.
Your next challenge
- Apply your expertise in data science techniques to develop predictive maintenance models and algorithms
- Acquire in-depth knowledge into cement plant operations, sensor data and maintenance engineering to know your data
- Work with IoT data sets, sensor data, and time-series data to identify patterns, extract valuable insights, and optimize maintenance schedules.
- Utilize time-series modeling techniques (e.g. ARIMA, SARIMAX, ET, LSTM, TimeGPT, regression models) to forecast equipment failure, detect anomalies, and mitigate potential risks.
- Collaborate with cross-functional teams to develop and implement Anomaly Detection solutions for enhancing fault detection capabilities.
- Implement MLOps practices to ensure seamless model deployment, monitoring, and performance optimization.
- Leverage Databricks to analyze big data
Your profile
- Master or PhD degree in Computer Science, Engineering, STEM or related
- A strong background in data science and machine learning, with hands-on experience in predictive maintenance
- Proficiency in working with IoT data, time-series data, and sensor data
- Familiarity with MLOps principles and tools for ensuring efficient model development, deployment, and maintenance
- Strong programming skills in languages such as Python, along with proficiency in data manipulation and analysis libraries.
- Excellent problem-solving skills and the ability to work collaboratively in cross-functional teams
- A solid understanding of statistical modeling, data visualization, and data-driven decision-making
Preferred Qualifications:
- Experience in the field of industrial manufacturing, equipment maintenance, or similar industries
- Knowledge of cloud platforms such as AWS, Azure, or Google Cloud
- Demonstrated expertise in building and deploying models for equipment failure prediction and anomaly detection
- Familiarity with additional tools and frameworks like TensorFlow, Keras, or PyTorch
- Experience using Databricks or similar big data processing platforms for analyzing and processing large-scale datasets and establishing MLOps workflows.
Our offer
We are convinced that only those who successfully realize their personal goals can also fully contribute professionally. That's why we offer you attractive benefits, such as:
- Attractive compensation including Christmas and vacation bonuses
- Flexible working time models
- Mobile working within Germany on up to three days per week
- 30 vacation days plus additional special vacation days
- Individual onboarding with participation in the buddy program
- Support for advanced training and continuing education
- Company health management (various company sports groups, health and prevention campaigns, company doctor)
- Ergonomic workstations with height-adjustable desks
- Canteen with discounted and healthy meals
- Employer-sponsored company pension plan
- Attractive conditions and discounts at our cooperation partners (gyms, banks, online stores, etc.)
- Job ticket