The Role
Develop and deploy machine learning and statistical models for predictive quality, maintenance, and energy optimization in metal manufacturing. Analyze industrial time-series data, engineer features, validate models, and collaborate with plant engineers and customers. Contribute to data onboarding, deployment, monitoring, reusable Python libraries, and MLOps practices. Mentor junior data scientists and communicate technical findings to technical and non-technical stakeholders.
Summary Generated by Built In
About TVARIT
TVARIT GmbH specializes in developing and delivering cutting-edge artificial intelligence (AI) solutions for the metal industry, including steel, aluminum, copper, cast iron, and more. Our software products empower customers to make intelligent, data-driven decisions, driving
advancements in Predictive Quality (PsQ), Predictive Maintenance (PdM), and Energy Consumption Reduction (PsE).
With a strong portfolio of renowned reference customers, state-of-the-art technology, a talented research team from prestigious universities, and recognition through esteemed awards such as the EU Horizon 2020 AI Prize, TVARIT is recognized as one of the most
innovative AI companies in Germany and Europe.
We are looking for a self-motivated person with a positive “can-do” attitude and excellent oral and written communication skills in English.
About the Role
We are looking for a Senior Data Scientist to join TVARIT’s Metals Division and build AI solutions that improve quality, yield, equipment reliability, and energy efficiency in metal manufacturing plants. The role suits a hands-on data scientist who has worked with real plant data, is fluent in time-series analysis and its tooling, and brings expertise in at least one metal manufacturing domain.
You will work directly with process data from customer plants, collaborate with process engineers and plant teams to understand the physics behind the data, and take models from exploration through validation and deployment. You will also contribute to reusable models and components that scale across plants and customers.
Key Responsibilities
Data Science & Modeling
• Develop, validate, and improve ML and statistical models for Predictive Quality, Predictive Maintenance, and energy optimization use cases in metal production and processing.
• Analyze high-frequency and batch time-series data from sensors, PLC/SCADA systems, historians, MES, and quality labs, including alignment of process signals with quality and batch or coil/heat records.
• Engineer features that capture process behavior, such as lags, rolling statistics, frequency-domain features, process-stage segmentation, and physically meaningful derived variables.
• Apply anomaly detection, forecasting, classification, regression, and root-cause analysis techniques, and combine data-driven models with process knowledge and physics-based constraints where appropriate.
• Design robust validation strategies for time-dependent data (e.g., time-based splits, leakage checks, drift analysis) and quantify model uncertainty and limitations clearly.
Plant & Customer Collaboration
• Work with plant process, quality, and maintenance engineers to understand process flows, operating conditions, and failure modes, and translate them into well-defined modeling problems.
• Assess data readiness and quality at customer sites, identify gaps in instrumentation or data capture, and recommend practical remedies.
• Present findings, model results, and recommendations clearly to plant teams and customer stakeholders, linking insights to measurable outcomes such as scrap, yield, downtime, and energy consumption.
Delivery & Productization
• Contribute to end-to-end project delivery, from data onboarding and exploratory analysis through model deployment, monitoring, and retraining, working closely with software and data engineering teams.
• Write clean, tested, reusable Python code and contribute to shared libraries, pipelines, and model templates that can be configured across plants and customers.
• Mentor junior data scientists, review their analyses and modeling approaches, and help establish best practices for industrial data science within the division.
Qualifications
• Bachelor’s or master’s degree in Data Science, Computer Science, Statistics, Mathematics, Metallurgical, Mechanical, Electrical, or Chemical Engineering, or a related discipline; a PhD is an advantage.
• 7+ years of overall professional experience, with substantial hands-on experience in data science or machine learning applied to industrial or manufacturing data.
• Mandatory manufacturing plant experience: has worked on data science projects with manufacturing plants, using real process, sensor, and quality data, and working directly with plant operations or engineering teams.
• Mandatory time-series expertise: strong practical experience in time-series data analysis, including preprocessing, resampling, alignment, feature engineering, anomaly detection, forecasting, and validation of time-dependent models.
• Metal manufacturing domain expertise: in-depth knowledge of at least one metal manufacturing domain, such as steelmaking (BOF/EAF, secondary metallurgy, continuous casting), aluminum (casting, rolling, extrusion), copper, foundry/die casting, forging, rolling, or heat treatment, including the key process variables and quality drivers.
• Proven track record of taking models beyond notebooks to validated, deployed solutions that delivered measurable business impact.
• Strong written and verbal communication skills, with the ability to explain technical results to both engineers and non-technical stakeholders.
• Willingness to travel to customer plants as required.
Technical Skills
• Programming: Strong Python skills for data science, with working proficiency in SQL; good coding practices including version control (Git), modular code, and testing.
• Data analysis libraries: pandas, NumPy, SciPy, and visualization libraries such as Matplotlib, Plotly, or Seaborn.
• Time-series and ML packages: Hands-on experience with packages such as statsmodels, scikit-learn, sktime, tsfresh, Darts, or Prophet, and gradient boosting libraries such as XGBoost or LightGBM.
• Deep learning: Experience with PyTorch or TensorFlow for sequence models (e.g., LSTM, temporal CNN, transformer-based models) is an advantage.
• Statistics and modeling: Solid foundation in statistics, signal processing, regression, classification, anomaly detection, forecasting, and design of
experiments; familiarity with hybrid physics-informed modeling is an advantage.
• Industrial data: Familiarity with PLC/SCADA data, historians (e.g., OSIsoft PI or similar), MES, and lab/quality systems, including data quality and contextualization challenges.
• Deployment and MLOps: Understanding of model deployment, monitoring, drift detection, and retraining; experience with tools such as MLflow, Docker, and cloud platforms (Azure preferred; AWS or GCP also relevant) is an advantage
Skills Required
- Bachelor's or master's degree in Data Science, Computer Science, Statistics, Mathematics, Metallurgical Engineering, Mechanical Engineering, Electrical Engineering, Chemical Engineering, or a related discipline
- 7+ years of overall professional experience
- Substantial hands-on data science or machine learning experience applied to industrial or manufacturing data
- Experience working on data science projects with manufacturing plants using real process, sensor, and quality data
- Experience working directly with plant operations or engineering teams
- Strong practical expertise in time-series data analysis, preprocessing, resampling, alignment, feature engineering, anomaly detection, forecasting, and time-dependent model validation
- In-depth expertise in at least one metal manufacturing domain, such as steelmaking, aluminum, copper, foundry, forging, rolling, or heat treatment
- Track record of taking models beyond notebooks to validated, deployed solutions with measurable business impact
- Strong written and verbal communication skills
- Willingness to travel to customer plants as required
- Strong Python skills and working proficiency in SQL
- Good coding practices, including version control, modular code, and testing
- Experience with industrial data sources including PLC/SCADA, historians, MES, and lab or quality systems
- Understanding of model deployment, monitoring, drift detection, and retraining
- PhD in a related discipline
- Experience with PyTorch or TensorFlow for sequence models
- Familiarity with hybrid physics-informed modeling
- Experience with MLflow, Docker, and cloud platforms such as Azure, AWS, or GCP
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The Company
What We Do
We are a specialized Deep Tech company based in Frankfurt, Germany, and a provider of cutting-edge TVARIT Industrial AI (TiA) Technology with a focus on the manufacturing industry, especially foundries and metalworking companies. Our sole mission is to enable a sustainable, zero-waste manufacturing using our technology by almost eliminating energy losses, waste and maximizing machine and equipment availability. Through our unique patented technologies like “Hybrid AI” & “Transfer learning,” we guarantee first results (on average -30% less scrap and -20% less energy consumption) within 2-3 months and thus an ROI under 6 months. With our technology TiA, we combat the knowledge drain at foundries and metal companies. The expert knowledge is "conserved" and continuously increased making the platform smarter and more accurate through longer use. We are creating "Digitally Empowered Operators": by using TiA less experienced operators can be "empowered" to make optimal recipe adjustments in real-time, ensuring virtually defect-free and non-disruptive production. Our customers include world-renowned manufacturing companies in the metal industry such as Aditya Birla, Kamax, Schunk Group, and Maxion Wheels. In addition to proven results from more than 55 industrial projects in reducing scrap, increasing machine availability & productivity as well as reducing CO2 emissions, we have been recognized as the best AI company in Europe in the track “Best Smart Factory Startup in Europe" (out of 8 tracks with 495 participants) by the European Data Incubator (EDI) in 2020. Check out our website www.tvarit.com for more details on what we do
