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Open nowPosted 30 days ago

ML Solution Engineer (Asset Management & Reliability)

MyCareersFuture94,028 open roles

Pay
SGD 7,000 – SGD 9,000 a month
Where
East, Singapore
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Your applicationOpen nowML Solution Engineer (Asset Management & Reliability)MyCareersFuture · East, Singapore

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The clock on this job

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Share of postings closed within
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  2. 3.3%3 days
  3. 7.7%7 days
  4. 14.0%14 days
  5. 33.7%30 days
This job: posted 30 days ago

The posting

Key Responsibilities

Machine Learning & Predictive Solutions

  • Develop, evaluate and deploy machine learning and predictive models for industrial asset management applications.
  • Apply machine learning techniques such as regression, classification, time-series analysis, anomaly detection and other relevant modelling approaches.
  • Develop predictive maintenance and equipment health monitoring solutions for early detection of equipment degradation and potential failures.
  • Analyse historical and real-time equipment, sensor and process data to identify patterns, anomalies and failure indicators.
  • Evaluate model performance and continuously improve model accuracy and reliability.
  • Explore the application of emerging AI technologies, including Generative AI, LLM and RAG, to enhance asset management solutions.

Asset Management & Reliability

  • Apply reliability engineering knowledge to support asset performance and predictive maintenance solutions.
  • Analyze asset performance using reliability indicators such as MTBF, MTTR, equipment availability and other relevant metrics.
  • Apply methodologies such as Reliability Centered Maintenance (RCM), Failure Mode and Effects
  • Analysis (FMEA) and Root Cause Analysis (RCA/RCFA) where applicable.
  • Work with asset and maintenance data to identify reliability risks and opportunities for performance improvement.
  • Support digitalization initiatives involving Asset Operations Management (AOM), Asset Performance Management (APM), condition monitoring and predictive maintenance.

Solution Development & Customer Engagement

  • Engage with customers to understand their asset management, reliability and operational challenges.
  • Gather and analyse customer requirements and translate them into appropriate ML and digital solution approaches.
  • Conduct technical discussions, workshops and solution demonstrations with customers.
  • Support solution scoping, feasibility studies, proof-of-concept (PoC) activities and technical proposal development.
  • Present analytical findings, ML model results and solution recommendations to customers and key stakeholders.
  • Support the implementation and delivery of ML-enabled asset management solutions.

Centre of Excellence & Collaboration

  • Work closely with other domain specialists to develop and enhance asset management solutions.
  • Provide technical and domain expertise in machine learning, predictive maintenance and asset reliability.
  • Contribute to the development of reusable ML models, methodologies, use cases and best practices within the Asset Management CoE.
  • Evaluate emerging AI/ML technologies and identify opportunities for application within industrial asset management.
  • Support knowledge sharing and capability development across regional teams and stakeholders.

Requirements

  • Bachelor's Degree in Computer Science, Data Science, or a related engineering/technical discipline.
  • At least 5 years of relevant experience in machine learning, data analytics, reliability engineering, asset management, predictive maintenance or industrial digital solutions.
  • Good understanding of machine learning techniques, statistical analysis and predictive modelling.
  • Experience with Python and relevant machine learning/data analytics tools and libraries.
  • Experience working with industrial equipment, machinery, sensor, process or time-series data would be advantageous.
  • Knowledge of asset reliability and maintenance methodologies such as RCM, FMEA, RCA/RCFA, MTBF and MTTR would be advantageous.
  • Exposure to Asset Operations Management (AOM), Asset Performance Management (APM), IIoT,
  • Digital Twin or condition monitoring technologies would be an advantage.
  • Knowledge or experience in Generative AI, LLM, RAG or other emerging AI technologies would be an added advantage.
  • Strong analytical and problem-solving skills with the ability to translate business and operational requirements into technical solutions.
  • Good communication and presentation skills with the ability to engage customers and collaborate effectively across multidisciplinary teams.

By responding to Yokogawa’s advertisement, consent is considered given to Yokogawa to collect the required personal data for the purpose of recruitment with expectation that Yokogawa will protect personal data with security safeguards that are reasonable and appropriate to the sensitivity of the personal data, to protect it from unauthorized access, use or disclosure and complies with applicable regulatory requirements with respect to the retention of personal data.

We regret to inform that only shortlisted candidates will be notified.

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