Lead Data Scientist

About Digis

Digis is a European IT company with 200+ specialists delivering complex SaaS products, enterprise solutions, and AI-powered platforms worldwide. We ensure transparency, stability, and professional growth opportunities for all our team members.

About the Project

A next-generation simulation and analytics platform for industrial utilities, designed to transform real-time sensor data into operational insights. The platform combines live field input, statistical inference, and event data to deliver explainable recommendations that improve performance, reduce maintenance costs, and optimize resource usage.This is a high-impact ML project — early-stage, research-driven, and focused on solving real engineering challenges, not just building LLM-based prototypes. You’ll join as a founding technical contributor and have long-term ownership over the analytics layer and modeling strategy.

Stack

Python, ML frameworks (PyTorch, TensorFlow, scikit-learn)

Data analytics & simulation tools, sensor data pipelines

Requirements

• 5+ years of commercial experience with Python and modern ML frameworks (PyTorch/TensorFlow/scikit-learn).
• 2+ years of experience building and operating production ML pipelines.
• 2+ years of experience with physics-informed or hybrid models and/or asset reliability analytics.
• Hands-on experience with time-series sensor data (anomaly detection, failure prediction).
• Experience in water networks or similar process industries (highly desirable).
• English level: Upper-Intermediate+

Responsibilities

• Lead the design and development of physics-informed and stochastic models for asset reliability and risk assessment.
• Build and validate machine learning models for anomaly detection, failure prediction, and system optimization.
• Analyze and interpret sensor and operational data to identify patterns and derive actionable insights.
• Implement data preprocessing, feature engineering, model training, validation, and explainability workflows using Python and modern ML frameworks.
• Collaborate with engineers, domain scientists, and product stakeholders to integrate analytics into digital twin and simulation platforms.
• Develop frameworks for uncertainty quantification, causal inference, and probabilistic reasoning to improve decision-making under uncertainty.
• Provide technical leadership, mentor junior data scientists, and establish best practices for scalable, production-grade ML pipelines.

We Offer

• 20 paid vacation days per year
• 5 paid sick leaves per year (no medical documents required)
• Personalized development plan with clear self-development goals
• Compensation for job-related trainings
• Work tools (PC, laptop, monitor) and workplace arrangement if needed
• Assistance with and compensation for English courses
• Flat, transparent internal communication
• Ability to switch between projects and technologies within the company
• Accounting support
• Free corporate psychologist services