Senior Data Scientist – AI, ML, Predictive System Maintenance

This is a fully remote position, open to applicants in Ukraine.

📋 Description

• Analyze historical data related to equipment, maintenance, operations, sensors, and telemetry to identify trends linked to equipment degradation and failures.

• Collaborate with domain specialists to comprehend failure modes, potential indicators, and assess if existing data offers adequate predictive signals.

• Conduct exploratory data analysis, data profiling, statistical assessments, correlation analysis, and feature discovery.

• Evaluate data quality, completeness, frequency, and predictive value prior to the development of models.

• Identify and create relevant features derived from time-series and operational data.

• Develop, train, validate, and optimize machine learning models for failure prediction, anomaly detection, and predictive maintenance.

• Analyze suitable modeling techniques for various equipment and failure scenarios.

• Establish appropriate model evaluation metrics and validate model performance against business and operational goals.

• Formulate strategies for addressing imbalanced datasets and rare failure occurrences.

• Work in conjunction with Data Engineers and solution architects to prepare data and integrate models.

• Partner with equipment and maintenance Subject Matter Experts (SMEs) to validate model outputs and ensure that predictions are operationally relevant.

• Promote model explainability and provide clarity on the key factors influencing predictions.

• Document experiments, assumptions, data limitations, model performance, and suggestions.

• Assist in transitioning validated models from experimental or proof-of-concept stages into production-ready solutions.

• Determine which equipment failures can realistically be forecasted using available data and establish connections between telemetry signals and failure modes.

• Construct validated predictive models that facilitate proactive maintenance decisions.

⛳️ Requirements

• 5+ years of experience in Data Science or Machine Learning, ideally with a focus on predictive maintenance, industrial analytics, or IoT applications.

• Strong expertise in Python and popular data science libraries such as Pandas, NumPy, and Scikit-learn.

• Comprehensive knowledge of machine learning, statistical analysis, time-series analysis, anomaly detection, classification, regression, feature engineering, and model validation and optimization.

• Experience handling sensor, telemetry, event, and time-series datasets.

• Familiarity with predictive maintenance principles, including failure prediction, condition monitoring, and equipment health indicators.

• Experience managing missing, noisy, sparse, and imbalanced industrial data.

• Strong SQL and data analysis capabilities.

• Experience with model explainability and presenting model results to both technical and business stakeholders.

• Ability to closely collaborate with engineering and domain SMEs to convert physical equipment behavior into analytical hypotheses.

• Prior experience with manufacturing, industrial equipment, IoT, vending or cooling apparatus, or connected-device analytics.

• Experience with cloud-based machine learning and data platforms.

• Familiarity with Azure Machine Learning, Databricks, MLflow, or similar technologies.

• Knowledge of MLOps and monitoring of production models.

• Experience with streaming or near-real-time telemetry.

• Understanding of Remaining Useful Life modeling and survival or reliability analysis.

• Experience with deep-learning techniques for time-series data.

🏝️ Benefits

• Health insurance from the very first day, irrespective of the probation period.

• Christmas holidays from December 25 to December 31.

• Collaboration with Superhumans center and Veteran HUB.

• Psychological counseling available through the Veteran Hub.

• Internal policies that make the company supportive of military personnel and veterans.