Specialist MSG Asset Health Developer

Date: Oct 27, 2026

Job Country: Australia

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About BHP

At BHP we support our people to grow, learn, develop their skills and reach their potential. With a global portfolio of operations, we offer a diverse and inclusive environment with extraordinary career opportunities. Our strategy is to focus on creating a safe work environment where our employees feel strongly connected to our values and objectives, and where the capability of our people is key to our success.

Come and be a part of this success.

About the Role

As the Specialist Asset Health Development, you will be responsible for the development and maintenance of predictive analytics models for Asset Health diagnosis and prognosis of BHP equipment. This role is Python-first and focused on time-series analytics (e.g., trending, deviations from trend, drift and early warning detection, baseline/expected behaviour bands), using enterprise historian data (e.g., AVEVA PI) and curated datasets in Snowflake to deploy repeatable, scalable analytics patterns across sites and asset classes.

Reporting to the Project Manager Predictive Maintenance, your key responsibilities will include:

1) Develop and maintain time-series Asset Health analytics

- Develop Python analytics scripts to identify degradation, drift, abnormal behaviour, and deviations from expected operating trends using equipment time-series and condition monitoring data.
- Perform time-series preparation and analytical feature engineering (e.g., resampling, smoothing/filters, rolling statistics, baseline modelling, rates-of-change, event-windowing)




- Apply statistical and/or machine learning techniques where appropriate, with a preference for interpretable and operationally trusted analytics that translate clearly to maintenance action.
- Produce clear documentation of logic, assumptions, thresholds, and limitations to support operational adoption and repeatability.

2) Scale analytics patterns across equipment types and sites

- Support delivery of the Asset Health program of work to scale and deploy analytics patterns to similar equipment types, leveraging consistent failure modes and monitorable symptoms with guidance from reliability engineers.
- Standardise reusable analytics patterns (templates, parameterisation, QA checks) that enable efficient replication across fleets and operating contexts.
- Monitor and optimise deployed analytics to improve alert quality (e.g., false positives, missed detections, lead time, stability across operating regimes).

3) Work with PI time-series data and Snowflake data products

- Use enterprise time-series historians (e.g., AVEVA PI / PI AF) to source, contextualise, and validate telemetry and event data for analytics development.
- Develop and maintain curated datasets and SQL logic in Snowflake to support repeatable analytics execution, monitoring, and reporting.
- Implement data quality checks and controls (e.g., missing data handling, sensor validity rules,



time alignment, outlier logic) to improve monitoring reliability and reduce noise.

4) Embed analytics into enterprise applications and lifecycle practices

- Support development of predictive Asset Health analytics within enterprise applications and workflows (alerting, dashboards, work identification), ensuring usability and operational fit.
- Monitor, evaluate and optimise the effectiveness of deployed analytics over time, including periodic recalibration as operating context changes.
- Work within agile delivery practices and adopt pragmatic lifecycle controls (version control, peer review, testing, documentation) to maintain quality and scalability.
- Support system improvement work and upgrades that increase monitoring quality and efficiency.

5) Reliability engineering collaboration and stakeholder engagement

- Establish understanding of failure modes, monitorable symptoms and rectifying actions for key mining and fixed plant equipment, partnering with reliability engineers and site teams.
- Collaborate with teams at operations and central groups to identify and develop high value asset health monitoring opportunities and communicate insights in an actionable way.
- Translate technical analytics into decision support that enables maintenance planning and risk-based action.

About You

- A cooperative problem-solving mindset with a passion for tackling complex maintenance challenges using data-driven insights.
- Strong hands-on experience with Python for time-series processing and analytical detection methods (trend, deviation, early warning logic) and automation of analytics workflows.

📌 Specialist MSG Asset Health Developer (Australia)
🏢 Bhp Group
📍 Australia