As a dynamic multinational tech company operating in 50 countries, we drive innovation and create projects that shape the future and greatly enhance the quality of life. You will find our solutions in the space industry, supporting scientists in the development of cancer drugs, and implementing innovative technological solutions for industrial clients worldwide. These are just some of the areas in which we operate!

Currently, for the new Seargin project we are looking for a Senior Data Scientist.

This role exists to remove a critical bottleneck in advanced data science capability within the Industrial IoT organization. Today, complex manufacturing initiatives depend on a single individual with deep mathematical and modelling expertise. The Senior Data Scientist will act as a multiplier, owning the most demanding analytical problems and enabling faster delivery of predictive quality, process optimization, and AI-driven automation across plants. This position is intentionally focused on mathematical rigor, model quality, and production impact, not reporting or dashboarding. We need a Senior consultant with +6 years of experience.

Contract type: B2B

Location: remote from Poland

Seniority: senior

Responsibilities:

Advanced Modelling & Predictive Analytics

• Design and implement predictive models for manufacturing use cases such as scrap detection, quality prediction, and process stability.
• Perform statistical and mathematical modelling of industrial processes using multivariate, time-series, and sensor data.
• Develop and validate algorithms that transform raw machine parameters into reliable, actionable predictions.
• Apply both supervised and unsupervised learning techniques to identify patterns, anomalies, and early indicators of quality issues.

Machine Learning & Deep Learning Implementation

• Build robust AI/ML models suitable for noisy, imperfect industrial data.
• Implement deep learning approaches where beneficial, including:
• CNN-based solutions for image-driven inspection
• Temporal models for machine and process behaviour
• Ensure models are explainable, measurable, and trusted by manufacturing stakeholders.

Production Deployment & Scalability

• Design and maintain end-to-end ML pipelines from data ingestion to deployment.
• Ensure scalability, performance, and monitoring of models in production environments.
• Contribute to architectural decisions related to model lifecycle management and integration into existing IoT platforms.

Industrial Collaboration & Ownership

• Act as the primary data science counterpart within the Industrial IoT team.
• Work hands-on with plant engineers, IoT developers, and product owners to translate real production problems into solvable data science tasks.
• Collaborate closely with the central Data Science organization to align methods, standards, and best practices.
• Operate as a right-hand partner on advanced data science topics, taking ownership of the most complex analytical challenges.

Requirements:

Programming & ML Tooling

• Strong proficiency in Python
• Practical experience with: scikit-learn, TensorFlow and/or PyTorch
• Ability to move models from experimentation to production-grade solutions

Data Handling & Engineering

• Advanced skills in: Data preprocessing and cleansing, Feature engineering for industrial time-series and process data
• Experience working with large datasets
• Strong SQL knowledge
• Familiarity with big data frameworks (Spark, Hadoop) is an advantage

Mathematics & Statistics

• Strong foundation in:
• Probability and statistics
• Linear algebra
• Optimization techniques
• Demonstrated ability to reason mathematically about manufacturing processes, not just apply standard ML recipes

Platform Requirements

• Mandatory: Solid hands-on experience with Microsoft Fabric
• Experience operating within Microsoft-based analytics ecosystems

Preferred Qualifications

• Master’s degree in Data Science, Computer Science, Statistics, Applied Mathematics, or a related field
• Experience in industrial IoT, manufacturing analytics, or quality engineering
• Familiarity with sensor data, machine data, and production KPIs

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