Specialist MSG Asset Health Developer

BHP Group; Australia; On-site; AUD 140,000 - 190,000; Full time

Job summary

BHP Group in Australia is seeking a Specialist Asset Health Developer to design and maintain predictive analytics for asset health using Python and time-series data from AVEVA PI and Snowflake datasets.

You will build scalable analytics templates, collaborate with reliability engineers, and embed insights into enterprise workflows, dashboards, and maintenance planning.

Qualifications

• Experience with time-series analytics in industrial maintenance.
• Strong Python skills for time-series processing and detection methods.
• Advanced SQL and Snowflake data handling for analytics datasets.
• Ability to validate analytics with stakeholders and iterate thresholds.

Responsibilities

• Develop and maintain time-series Asset Health analytics.
• Scale analytics patterns across equipment types and sites.
• Work with PI time-series data and Snowflake data products.
• Embed analytics into enterprise applications and workflows.
• Collaborate with reliability engineers and site teams.

Skills

Python time-series

SQL

Snowflake

Stakeholder engagement

Communication

Problem solving

Tools

AVEVA PI

PI AF

Snowflake

Job description

Specialist MSG Asset Health Developer

Date: Oct 27, 2026

Job Country: Australia

Job State/Province:

Job Location/Region:

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 collaborative 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.
• Strong SQL skills and experience working with Snowflake (or comparable cloud data platforms) to build performant, reliable analytics datasets.
• Familiarity with enterprise time-series data historians (e.g., AVEVA PI / PI AF) and industrial telemetry challenges (sampling, gaps, bad values, context).
• Ability to validate analytics with operational stakeholders, tune thresholds, and manage performance over time (monitoring, continuous improvement).
• Excellent communication and stakeholder engagement skills, with the ability to translate technical insights into actionable business outcomes.

Highly Desirable - Reliability Engineering (Industrial Applications)

• Demonstrated reliability engineering experience in asset-intensive / industrial environments (e.g., mining, minerals processing, fixed plant, heavy mobile equipment).
• Strong understanding of reliability methods such as Reliability Centred Maintenance (RCM), Failure Mode & Effects Analysis (FMEA), and Root Cause Analysis (RCA), and ability to link failure modes to monitorable symptoms and recommended actions.
• Experience interpreting and applying condition monitoring information (e.g., oil analysis, vibration analysis, sensor diagnostics) in an operational reliability context.

• Experience working in agile development environments and applying practical lifecycle discipline for analytics (version control, testing, documentation, release practices).
• Experience with node-based analytics / model orchestration tools is advantageous.
• Engineering or trade background with proven experience in relevant fields is advantageous.

About Our Process

At BHP