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Information Technology - Senior Data Scientist (Advanced AI Track) (SG)

Reads like an AI Engineer role: building products on existing AI models.

Pay
Pay not listed
Work setting
Not stated
Employment
Not stated

What you’ll work on

Full posting
  • Build and operate shared AI platforms such as an LLM gateway and agent orchestration layer

  • Deliver LLM and agent applications from prototype to live service for business units

  • Evaluate AI systems and define release gates, rollback criteria and model upgrades

  • Apply traditional ML and optimization to fraud detection, forecasting and document classification

  • Plan and run AI projects end to end, with status and risk reporting to management

From the employer’s posting
Build and operate shared AI platforms: a multi-provider LLM gateway, an agent orchestration layer
Deliver LLM and agent applications for business units, from prototype to live service with measurable business results.
Evaluate AI systems: build test sets; measure groundedness, factual accuracy, task-success rate, safety, latency, reliability and model or data drift; define release gates and rollback criteria
Apply traditional machine learning and optimization to fraud and anomaly detection, forecasting, resource optimization and document classification.
Plan and run AI projects end to end: scope, milestones, cross-team coordination, and clear status and risk reporting to management.
Apply at Singapore Airlines

Posting record

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Actively hiringFirst seen by us 8 days ago, still listed, and not a repost of an earlier posting.

Status in our records
Live
First seen by us
Nov 5, 20268 days ago
Recorded sightings
9
Last seen by us
Nov 13, 2026

These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.

How this job compares

Time since we first saw it

Day 8

Similar jobs usually come down between day 4 and day 77.

From 51 similar postings that came down. Coming down is not always a hire.

Singapore Airlines is hiring

2 open jobs

Open jobs by the week we first saw them, last 12 weeks.

Hiring record

See how this role fits your experience

Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.

  • Python
  • SQL
  • AWS
  • Azure
  • GCP
Where the posting names them
- Degree in computer science, machine learning or a related field, or equivalent experience. - Strong Python and SQL; 3+ years delivering ML or AI systems to production. - Traditional ML: gradient-boosted trees, regression, forecasting, clustering, anomaly detection, feature engineering, validation, and knowing when a simple model is better.
- Traditional ML: gradient-boosted trees, regression, forecasting, clustering, anomaly detection, feature engineering, validation, and knowing when a simple model is better. - LLM applications with commercial or open-weight models (Claude, GPT, Gemini, AWS Bedrock): prompts, retrieval, tool calling and multi-step agents. - Structured evaluation and monitoring of LLM systems in production (test sets, automated checks, quality, safety, latency, reliability, drift), including tool-call accuracy and policy compliance for agents.
- Responsible AI: measurable controls such as guardrails, groundedness checks and audit trails, plus data privacy and access control (for example PDPA). - Secure cloud deployment (AWS, Azure or GCP) and cost management. - Experience taking an AI or analytics solution beyond launch and measuring its impact on revenue, cost, productivity, service quality or operations.

Your next step

Complete your application on careers.singaporeair.com. The employer’s form will show what is required.

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Demand for AI Engineer roles

New AI Engineer postings at the employers we follow are up 134% over the last 6 weeks compared with the 6 before.

Weekly new AI Engineer postings at the same employers, from the week of August 17 to the week of November 2. The fewest in a week was 7, the most 65, and the latest week had 65.
August 17Last week

Each bar is one week of new postings. The darkest bars are the last 6 weeks.

Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.

About the role

We file this job under AI Engineer, by the work the posting describes.

AI engineers build products on top of AI models that already exist, most often large language models: they connect a model to a company's data and tools and ship the assistants, agents and search features people use.

Postings ask for strong Python or TypeScript, experience with model APIs, retrieval and agent frameworks, and ways to test whether the model's answers are good enough.

An ML engineer trains and serves models; an AI engineer usually starts from a model someone else trained and builds the product around it, though titles often mix the two.

Job description

View original posting ↗

Job Description

Join SIA's in-house AI Center of Excellence, which runs the company's shared AI platforms and delivers LLM and AI agent products to business units.

 

Key Responsibilities

  • Build and operate shared AI platforms: a multi-provider LLM gateway, an agent orchestration layer, an evaluation and monitoring layer, and a staff assistant.
  • Deliver LLM and agent applications for business units, from prototype to live service with measurable business results.
  • Evaluate AI systems: build test sets; measure groundedness, factual accuracy, task-success rate, safety, latency, reliability and model or data drift; define release gates and rollback criteria; manage model upgrades and retirements.
  • For agentic workflows, evaluate tool-call accuracy, policy compliance, unauthorised-action prevention and human-escalation effectiveness.
  • Balance cost and performance: route between models, use batch processing, and run models on our own GPUs when data cannot leave the company.
  • Design how agents retrieve and share business knowledge, with access control and data governance across SIA and Group companies.
  • Apply traditional machine learning and optimization to fraud and anomaly detection, forecasting, resource optimization and document classification. Decide when a classic model beats an LLM.
  • Work with Data Engineering, Enterprise Architecture and other teams as the technical go-to person in your area.
  • Work with business units to define the problem and success measures, explain results in plain language, and confirm the business outcome after launch.
  • Plan and run AI projects end to end: scope, milestones, cross-team coordination, and clear status and risk reporting to management.

 

Requirements: Must Have

  • Business communication: you find the real problem behind a request, explain limits without jargon, and renegotiate scope when needed.
  • Curiosity: you try new models and tools on real problems, and investigate the cause of unexpected behaviour.
  • Logical thinking: you break vague problems into parts, state assumptions, and support conclusions with evidence.
  • Can-do attitude: you take ownership, find a way forward when the first approach fails, and raise risks early.
  • Project management: milestones, realistic estimates, stakeholder updates, and plan changes across more than one team.
  • Mentoring skills to guide junior data scientists.
  • Degree in computer science, machine learning or a related field, or equivalent experience.
  • Strong Python and SQL; 3+ years delivering ML or AI systems to production.
  • Traditional ML: gradient-boosted trees, regression, forecasting, clustering, anomaly detection, feature engineering, validation, and knowing when a simple model is better.
  • LLM applications with commercial or open-weight models (Claude, GPT, Gemini, AWS Bedrock): prompts, retrieval, tool calling and multi-step agents.
  • Structured evaluation and monitoring of LLM systems in production (test sets, automated checks, quality, safety, latency, reliability, drift), including tool-call accuracy and policy compliance for agents.
  • Responsible AI: measurable controls such as guardrails, groundedness checks and audit trails, plus data privacy and access control (for example PDPA).
  • Secure cloud deployment (AWS, Azure or GCP) and cost management.
  • Experience taking an AI or analytics solution beyond launch and measuring its impact on revenue, cost, productivity, service quality or operations.

 

We thank all candidates for your interest in Singapore Airlines, and regret that only shortlisted candidates will be notified.

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Singapore

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