Senior Data Scientist, Predictive Maintenance System

Job not on LinkedIn

🔥 2 hours ago

🇺🇦 Ukraine – Remote

⏰ Full Time

🟠 Senior

📊 Data Scientist

👻 Ghost score 18%

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Capgemini

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1967

💼 Consulting

🏥 Healthcare

📦 Logistics

Consulting • Healthcare • Logistics

Capgemini is a global leader in partnering with businesses to transform and manage their operations by harnessing the power of technology. With expertise across a wide array of industries such as aerospace, automotive, banking, and healthcare, Capgemini provides a constantly evolving portfolio of services to meet the ever-changing needs of their clients. Their offerings include cloud, cybersecurity, data and artificial intelligence, and enterprise management, among others. Capgemini also emphasizes innovation and sustainability, helping companies achieve digital transformation while promoting environmental and social responsibility. Additionally, Capgemini provides career opportunities across various levels and professions, encouraging innovation and diversity in its workforce.

📋 Description

• 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

🎯 Requirements

• 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 • 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

🏖️ Benefits

• Health insurance from the first days, regardless of the probationary period • Christmas holidays from 25 December to 31 December • Cooperation with Superhumans center and Veteran HUB • Psychological counseling provided by the Veteran Hub • Internal policy making the company friendly to military and veterans • Support for psychological rehabilitation initiatives • Permanent contract

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