{"id":1249603,"url":"https://alion.io/job/keppel-data-scientist-aiml-predictive-maintenance-platform","title":"Data Scientist, AI/ML Predictive Maintenance Platform","company":{"id":451670,"name":"Keppel","domain":"keppel.com","url":"https://alion.io/company/keppel","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":75,"open_postings":55,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-09-29T05:45:00Z"}},"role":"Data Science","role_family":"Data Science","seniority":"middle","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Singapore"],"countries":["SG"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":46000,"max_usd":94000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":14},"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Anomaly Detection","optional":false},{"name":"Machine Learning","optional":false},{"name":"Time Series Forecasting","optional":false},{"name":"Transfer Learning","optional":false}],"status":"live","first_seen_at":"2026-09-02T00:00:00Z","employer_posted_date":"2026-09-02","last_verified_at":"2026-09-29T23:42:07Z","board_verified":true,"closed_at":null,"days_open":28,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":28},"description":"JOB DESCRIPTION\nWe are looking for a Data Scientist to build and operationalize machine learning solutions for failure prediction and early-warning detection across data center and industrial assets.\nThe role focuses on identifying degradation patterns, forecasting asset health, detecting anomalies, and estimating time to failure from sensor data. Success requires strong experimentation skills, the ability to work with limited failure examples, and a disciplined approach to validating models in real-world environments.\nCandidates should demonstrate both technical depth and a strong research mindset.\nKey Responsibilities\n1. Predictive Maintenance & Early-Warning Model Development\nDevelop and validate models using operational sensor data for failure prediction, asset (mechanical and electrical equipment) degradation monitoring, early-warning detection, remaining useful life estimation, and time-to-threshold forecasting.\nApply and compare survival analysis, time-series forecasting, anomaly detection, change-point detection, and representation learning techniques.\nSelect modelling approaches using measurable performance criteria and documented experimental results.\nBalance detection performance, false-positive rates, explainability, and production deployment requirements.\n2. Working with Sparse Failure Data\nDevelop strategies for environments where no failure events.\nWork with limited labelled failures, class imbalance, weak supervision, proxy labels, transfer learning, and failure-data sourcing.\nEstablish validation methods that accurately measure model effectiveness despite limited failure examples.\n3. Experimentation & Research\nForm hypotheses and design experiments to evaluate competing approaches.\nDefine baseline approaches, evaluate alternatives, measure improvements using objective metrics, and document findings and limitations.\nJustify model selection using evidence rather than preference.\n4. Model Productionization & Monitoring\nDeploy models into development and production environments.\nMonitor precision, recall, drift, and data quality.\nEstablish retraining, rollback, and retirement criteria for production models.\nInvestigate performance degradation and implement corrective actions.\n5. Cross-Functional Collaboration\nCollaborate with product managers, software engineers, platform engineers, and subject matter experts (data center domain with mechanical and electrical engineering expertise)\nTranslate operational problems into machine learning problems.\nIncorporate domain expertise into model development, validation, and alert interpretation.\nJOB REQUIREMENTS\nRequirements\nBachelor’s or Master’s degree in Engineering, Computer Science, Data Science, or a related field; specialization in AI/ML is preferred. Mechanical engineering background or strong exposure to mechanical/industrial systems will be an advantage.\n4-7 years of relevant experience in data science, or applied machine learning, either in a large software development organization or an end-user engineering/industrial environment.\nMinimum 2-3 years of hands-on experience developing failure prediction, anomaly detection, condition monitoring, or predictive maintenance models using engineering, operational, or IoT sensor data.\nGood understanding of common machine learning approaches and the intuition behind them, including time-series models, anomaly detection methods, classification/regression models, and model evaluation techniques.\nBUSINESS SEGMENT\nConnectivityPLATFORM\nOperating Division","description_format":"text","description_chars":3514,"description_truncated":false,"requirements":{"experience_years_min":4,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Industrial IoT (IIoT)","Property Development","Asset Management & Funds","Telecom Infrastructure"],"lifecycle":[{"event":"open","at":"2026-09-25T18:15:57Z"}],"liveness":{"score":53,"band":"ok","label":"Likely open","p_open":1,"p_active":0.588,"p_room":0.9,"age_days":27,"expected_fill_days":42,"reasons":["conf:11","stale_co","velocity","win:mid","comp:brand"],"computed_at":"2026-09-29T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/keppel-data-scientist-aiml-predictive-maintenance-platform","json_url":"https://alion.io/job/keppel-data-scientist-aiml-predictive-maintenance-platform.json","meta":{"generated_at":"2026-09-30T05:39:40Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":4087,"day_limit":5000,"remaining_today":913,"minute_limit":60,"resets_at":"2026-10-01T00:00:00Z"}}}