Senior Data Scientist (Predictive Maintenance system)

At Capgemini Engineering, the world leader in engineering services, we bring together a global team of engineers, scientists, and architects to help the world’s most innovative companies unleash their potential. From autonomous cars to life-saving robots, our digital and software technology experts think outside the box as they provide unique R&D and engineering services across all industries. Join us for a career full of opportunities. Where you can make a difference. Where no two days are the same.

YOUR ROLE

We are looking for an experienced Data Scientist to support the design and implementation of an AI-driven Predictive Maintenance solution for industrial equipment.

The role will focus on analyzing historical and telemetry data, understanding equipment failure patterns, assessing data suitability for predictive use cases, developing machine learning models, and translating analytical findings into actionable maintenance insights.

Expected Contribution

The Data Scientist will help determine which equipment failures can realistically be predicted from the available data, establish the relationship between telemetry signals and failure modes, and build validated predictive models capable of supporting proactive maintenance decisions.

YOUR TASKS

• Analyze historical equipment, maintenance, operational, sensor and telemetry data to identify patterns associated with equipment degradation and failures.
• Work with domain experts to understand failure modes, their potential indicators, and whether available data provides sufficient predictive signals.
• Perform exploratory data analysis, data profiling, statistical analysis, correlation analysis and feature discovery.
• Assess data quality, completeness, frequency and predictive value before model development.
• Identify and engineer relevant features from time-series and operational data.
• Develop, train, validate and optimize machine learning models for failure prediction, anomaly detection and predictive maintenance.
• Evaluate appropriate modeling approaches for different equipment and failure scenarios.
• Define suitable model evaluation metrics and validate model performance against business and operational objectives.
• Develop approaches for handling imbalanced datasets and rare failure events.
• Collaborate with Data Engineers and solution architects on data preparation and model integration.
• Collaborate with equipment and maintenance SMEs to validate model outputs and ensure predictions are operationally meaningful.
• Support model explainability and provide transparency into the major factors contributing to predictions.
• Document experiments, assumptions, data limitations, model performance and recommendations.
• Contribute to the transition of validated models from experimentation or proof of concept into a production-ready solution.

YOUR PROFILE

• 5+ years of experience in Data Science or Machine Learning, preferably including predictive maintenance, industrial analytics or IoT use cases.
• Strong proficiency in Python and common data science libraries such as Pandas, NumPy and Scikit-learn.
• Strong knowledge of machine learning, statistical analysis, time-series analysis, anomaly detection, classification, regression, feature engineering, and model validation and optimization.
• Experience working with sensor, telemetry, event and time-series datasets.
• Understanding of predictive maintenance concepts, including failure prediction, condition monitoring and equipment-health indicators.
• Experience dealing with missing, noisy, sparse and imbalanced industrial data.
• Strong SQL and data-analysis skills.
• Experience with model explainability and communicating model results to technical and business stakeholders.
• Ability to work closely with engineering and domain SMEs to translate physical equipment behavior into analytical hypotheses.

Nice to Have

• Previous experience with manufacturing, industrial equipment, IoT, vending or cooling equipment, or connected-device analytics.
• Experience with cloud-based ML and data platforms.
• Experience with Azure Machine Learning, Databricks, MLflow or equivalent technologies.
• Knowledge of MLOps and production model monitoring.
• Experience with streaming or near-real-time telemetry.
• Understanding of Remaining Useful Life modeling and survival or reliability analysis.
• Experience with deep-learning approaches for time-series data.

What you will love about working here?

• We care about all our employees and want them to feel as comfortable as possible. That's why we offer them health insurance from the first days, regardless of the probationary period.
• The gift from the company - Christmas holidays from 25 December to 31 December.
• Сooperation with Superhumans center and Veteran HUB. Capgemini Engineering has supported the launch of psychological rehabilitation department of Superhumans. Our team also donnated over UAH 500 000 prosthetics for three Ukrainian defenders. Currently, we support psychological counseling provided by the Veteran Hub, and we have implemented a internal policy making the company friendly to military and veterans with the assistance of the Hub.

Capgemini is the business transformation partner for enterprises in the age of AI. We help organizations imagine and build an intelligent, sustainable future, combining AI, technology and human ingenuity to transform how they operate, innovate and grow. With unique end-to-end capabilities spanning strategy, technology, engineering and intelligent operations, we bring together deep industry expertise and market-leading capabilities in AI, cloud and data to turn ambition into measurable business outcomes at scale. Supported by a robust ecosystem of partners and nearly 60 years of expertise, Capgemini is a responsible and diverse global organization of over 410,000 team members in more than 50 countries.