Data Science Lead - Part-time - EMEA

Company

Aqua Alarm delivers real‑time water quality insights for utilities. Work spans: sensor/data acquisition, models & algorithms, and a SaaS condition‑monitoring platform. Active pilot in Wales. Teams include 3 hydro engineers (NO/FIN/ID) and a hardware/infra team in Poland.

About the Role

We are seeking an experienced Data Science Lead to drive the development of advanced analytics and reliability models for industrial systems. This role focuses on applying statistical modeling, inference, and reliability analytics to improve the performance, integrity, and risk management of complex assets — with a strong preference for candidates familiar with water networks, water quality, or similar process industries.
The successful candidate will bridge data science and domain expertise, helping to design the next generation of digital twin and simulation tools that integrate real-time sensing, system knowledge, and predictive reliability insights.

Key Responsibilities

• Lead the design and development of physics-informed and stochastic models for asset reliability and risk assessment.
• Develop and validate machine learning models for anomaly detection, failure prediction, and system optimization.
• Analyze and interpret sensor and operational data to derive actionable insights on asset performance and degradation.
• Implement data conditioning, feature engineering, model training, validation, and interpretability workflows using Python and modern ML frameworks.
• Collaborate with engineering, operations, and domain experts to integrate analytics into digital twin and simulation platforms.
• Contribute to uncertainty quantification, causal inference, and probabilistic reasoning frameworks to enhance decision-making under uncertainty.
• Provide technical leadership, mentor data scientists, and establish best practices for scalable and reliable analytics pipelines.

Qualifications & Experience

• (Ideal) Master’s or PhD in Data Science, Statistics, Applied Mathematics, Engineering, or a related field.
• Proven experience (5+ years) in statistical modeling, reliability analytics, and risk management for industrial or infrastructure systems.
• Strong proficiency in Python and modern ML frameworks (e.g., scikit-learn, PyTorch, TensorFlow).
• Hands-on experience working with sensor data and developing physics-informed or hybrid modeling approaches.
• Deep understanding of uncertainty quantification, causal inference, and probabilistic reasoning.
• Experience in deploying and scaling analytical models in production environments.
• Familiarity with water networks, water quality systems, or similar process industries is highly desirable.