Specialist MSG Asset Health Developer | Australia

BHP; Australia; On-site; AUD 120,000 - 190,000; Full time

Benefits offered by this job

Flexible working options

Generous paid parental leave

Extended leave entitlements

Parent rooms

Job summary

BHP is seeking a Specialist Asset Health Development professional in Western Australia to develop and maintain predictive analytics for Asset Health across equipment families. The role is Python-first, focused on time-series analysis, leveraging AVEVA PI data and Snowflake datasets to deploy scalable analytics patterns for maintenance actions.

You will collaborate with reliability engineers and site teams to translate insights into actionable maintenance planning, while ensuring data quality,

Qualifications

• Collaborative problem-solving mindset applied to complex maintenance challenges using data-driven insights.
• Hands-on experience with Python for time-series processing and anomaly detection.
• Strong SQL skills and Snowflake experience to build analytics datasets.
• Familiarity with AVEVA PI / PI AF for industrial telemetry.
• Ability to validate analytics with stakeholders and tune thresholds.
• Excellent communication and stakeholder engagement skills.

Responsibilities

• Develop and maintain time-series Asset Health analytics.
• Develop Python scripts to identify degradation and deviations from trend.
• Perform time-series prep and feature engineering (resampling, smoothing, baselining).
• Apply statistical or machine learning techniques with emphasis on actionable results.
• Document logic, thresholds, and limitations for repeatability.
• Scale analytics patterns across equipment types and sites.
• Support deployment of Asset Health analytics within enterprise apps and dashboards.
• Standardise reusable analytics templates and QA checks.
• Monitor deployed analytics to improve alert quality and lead time.
• Work with PI time-series data and Snowflake data products.
• Develop curated datasets and SQL logic to support analytics execution.
• Implement data quality checks and controls.
• Collaborate with reliability engineers and site teams.
• Translate analytics into maintenance planning and risk-based actions.

Skills

Time-series processing

Python analytics

Automation of analytics workflows

SQL

Data validation

Stakeholder engagement

Education

Engineering or trade background

Tools

Python

SQL

Snowflake

AVEVA PI / PI AF

Job description

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:

• 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.
• 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).
• 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.
• 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.
• 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

Core capabilities (Essential)

• 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.

Desirable (Additional)

• 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, we are committed to employing individuals who align with the BHP Charter Values and meet the requirements of the role. As part of the recruitment process, there are a number of checks which may be conducted to demonstrate applicants suitability for a role including police / criminal background checks, medical, drug and alcohol testing, due diligence checks, right to work checks, and/or reference checks.

If you are already employed directly by BHP, please log in using your BHP email address or apply via our internal jobs portal.

Supporting a Diverse Workforce

• These benefits include flexible working options, a generous paid parental leave policy, other extended leave entitlements and parent rooms.
• We are an Equal Opportunity employer that is committed to making BHP a safe and inclusive workplace where everyone can thrive and be at their best every day. We are focused on creating a workforce that’s more diverse and represents the communities where we work and live. providing a work environment in which everyone is included, treated fairly and with respect.
• We are an Equal Opportunity employer and recognise that true diversity includes gender, age, race, disability status, sexual orientation, religion, neurodiversity, education levels, and many more aspects of your identity.
• BHP is committed to providing a recruitment process that is fair, equitable and accessible for all. If you have a disability, we know that it may be helpful for us to adjust our process to make it equitable for your individual situation. If you would like to reach out to someone about your situation and our recruitment process, please email us at [e-mail].