Project Overview
We''re seeking an experienced Data Engineer / AI Consultant to design and implement advanced reliability (“life”) models using historical service labor, parts usage, and maintenance event data.
This engagement will enable improved reliability insights, optimized service strategy, more accurate parts planning, and enhanced product quality through data-driven analysis. The project focuses on developing statistically grounded reliability estimates, identifying early-life failure behavior, and generating usage-based performance insights across critical systems and components.
Objectives
The consultant will create a scalable data and modeling framework that allows the organization to:
Quantify usage-based life characteristics (e.g., operating time, cycles, or utilization signals) for major assemblies and components.
Detect early-life failure trends using operational metrics, maintenance records, and service history.
Build reliability models that reflect real-world field performance across multiple product lines.
Establish a repeatable analytical approach that internal technical teams can maintain and expand after project completion.
Scope of Work
Data Engineering & Preparation
Aggregate, cleanse, and transform historical operational and maintenance data from enterprise service management platforms, including:
Maintenance and repair records
Failure classifications
Replacement part usage
Equipment configuration attributes
Operational signals such as cycles, runtime, or activity counts
Develop a consolidated dataset optimized for reliability analysis and statistical modeling.
Assess data completeness, identify inconsistencies, and recommend remediation strategies where needed.
Reliability & Life Modeling
Develop statistical reliability models using techniques such as Weibull analysis, survival modeling, and time-to-failure estimation.
Generate usage-based reliability curves for key subsystems and components.
Produce statistical reliability outputs including:
B10 / B50 life metrics
Early-life failure probability estimates
Failure distribution profiles
Reliability trends segmented by region, product category, and usage characteristics
Validate models for accuracy, robustness, and practical applicability.
Predictive Model Development
Design predictive algorithms that estimate failure likelihood based on real-world operating conditions.
Identify leading indicators that provide early warning signals for potential component or system failures.
Apply machine learning or regression-based techniques where appropriate to improve forecasting accuracy.
Deliverables & Knowledge Transfer
Structured, analysis-ready reliability dataset derived from historical service records.
Usage-based life models (including Weibull and B10 estimates) for targeted components and systems.
Analytical framework for identifying early-life failure patterns using operational and maintenance data.
Comprehensive documentation package including:
Data definitions and structure overview
Modeling approach and methodology
Assumptions and validation procedures
Reusable code assets or notebooks (Python and/or SQL)
Final summary presentation outlining key insights, reliability findings, and strategic recommendations.
Never miss an opportunity! Create an alert based on the job you applied for.
