Better data hires, better decisions.
Data and AI professionals close the gap between what a model achieves in testing and what it delivers in production. With the right hires, your platforms carry clean and timely data, your models run with the controls your risk teams expect, and your leadership gains a data capability it can build strategy on.
We specialise in data and analytics recruitment for financial services and hedge funds across Singapore.
Data & analytics recruitment in financial services
Each of our consultants specialises in one area rather than working as a generalist. In data and analytics, that means keeping our market knowledge current every day, tracking how platforms such as Databricks, Snowflake and kdb+ are reshaping teams, how new roles like AI engineer and ML platform engineer are defined, and how pay is moving. Our executive search service extends to Head of Data and Chief Data Officer appointments, where leadership and influence matter as much as technical background.
The strongest hires add value beyond their job description, so we look at the whole picture. Technical depth in Python, SQL, cloud data platforms and model deployment is the foundation, alongside:
- Sound judgement: knowing when a result is ready to act on.
- Clear explanation: making models understandable to traders, risk teams and leadership.
- Care with data: building in governance from the start.
Every candidate is screened by a market-expert consultant who looks past titles and certifications to what they have delivered.
We recruit across data and AI, from analytics through to production machine learning:
- Artificial Intelligence: AI engineers, generative AI specialists and AI product leads bringing large language models and agentic tools into regulated workflows.
- Data Analytics: BI developers, product analysts and insight leads who give trading, risk and commercial teams information they can act on.
- Data Engineering: data engineers, platform engineers and data architects who build the foundations every other data role depends on.
- Data Science: data scientists and quantitative modellers working on credit, fraud, pricing, customer and market problems.
- MLOps & Machine Learning: ML and MLOps engineers who keep models in production monitored, reproducible and auditable.
Building data capability in-house at a global bank, hedge fund or multinational corporation is as much about team design as individual skills. We look at where data engineering sits alongside data science, how the platform team supports the business, and which roles should be permanent to keep knowledge in the organisation. We can also help you decide where contract expertise speeds up a platform migration or model build, and how to sequence hires so your programme keeps its pace.
For consultancies and professional services firms delivering data and AI programmes for financial services clients, the focus shifts to skills and client readiness. New hires need to step straight into a client environment, and requirements on platforms, domain experience or certifications can be specific. We work to those constraints, alongside start dates, contract length, notice periods, garden leave for moves between funds and work pass considerations for candidates relocating to Singapore.
Whichever model fits, we offer permanent, contract and executive search hiring. Our permanent recruitment and contract hiring pages explain each in more detail.
We support organisations where data and AI shape financial and commercial decisions:
- Global banks: risk and regulatory data, data platforms and AI adoption across the bank.
- Hedge funds: market and alternative data, and the research infrastructure behind investment teams.
- Fintech: payments, digital banking and wealth platforms where data supports fraud prevention and credit decisions.
- FMCG & consumer goods: regional data teams within multinational corporations, working on forecasting, pricing and customer insight.
- Professional services: advisory teams delivering data and AI programmes for financial services clients.
A specialist recruiter brings an understanding of how data and AI work is done in financial services to every stage of hiring, from shaping the brief to agreeing the offer. Our Singapore office has been part of the market since 2014, and our network of 900,000+ candidates gives each search a wide starting point.
Our consultants look for evidence of what candidates have built, deployed and governed. Before anyone reaches your interview stage, they explore how each candidate has worked with risk, compliance and business stakeholders. We share up-to-date salary insight, realistic availability and a clear view of how similar profiles are moving in Singapore, so you can plan with confidence. Our Singapore teams also recruit across banking and insurance, so we can draw on that wider view when a role sits between data and the business.
Data & analytics market intelligence
Current market data points to rising demand for AI, Python, SQL and cloud skills across Singapore’s technology roles. The right mix for you depends on your organisation, your platform and how mature your data function is.
At platform level, cloud environments on AWS, Azure or Google Cloud sit alongside Databricks, Snowflake and Spark, with Airflow, dbt and Kafka shaping how pipelines are built and run. In hedge funds and trading environments, kdb+/q and time-series data experience can be decisive. For machine learning in production, MLflow, Kubernetes and model monitoring come into focus, alongside large language model frameworks for AI roles. Analytics teams continue to rely on Power BI and Tableau.
Our consultants explore how candidates have used these tools in practice. We ask how they have handled data quality at scale, taken a model through validation into production, or rebuilt a pipeline without disrupting the business. In regulated settings, we also explore how they have worked within model risk, data governance and audit expectations, because that experience comes from delivery rather than training.
Data and AI pay in Singapore varies by specialism, seniority and employer. At mid-level, AI and MLOps expertise sits at the top of the range, followed by data science and ML engineering, then data engineering and data analytics. As a guide, annual salaries for professionals with four to eight years’ experience are:
- Data analytics: SGD 100,000 to 145,000
- Data engineering: SGD 120,000 to 180,000
- Data science and ML engineering: SGD 135,000 to 200,000
- AI specialist and MLOps: SGD 160,000 to 240,000+
Contract roles are priced as a rate rather than an annual package, so the two do not compare directly. Our benchmarking page offers tailored figures by role, seniority and employer type.
AI is turning governance into a skill you hire for. Singapore’s financial regulator has set out supervisory expectations for how financial institutions identify, assess and control their use of AI, covering traditional machine learning, generative AI and AI agents. The expectations take effect from October 2027, with phased implementation allowed until October 2028.
That changes the profile of the people who make AI work. Building a model is one part of the role. Keeping an accurate inventory of AI use, testing and monitoring models through their lifecycle, overseeing third-party AI and explaining outcomes to senior management are now part of the brief too. MLOps engineers, data engineers who understand lineage, and data scientists comfortable with validation all become central to an AI programme rather than supporting it.
Personal data protection rules still apply to the data your models use, and hedge funds and multinational corporations add their own governance expectations. Exploring a model a candidate has documented, monitored or retired in production is one of the clearest ways to see how they will work within these expectations.
Singapore is investing in AI skills at national scale, including a programme to upskill tens of thousands of technology professionals over three years in advanced areas such as agentic AI deployment. Current market data also shows a gap between the number of workers who feel they need AI skills and the number who have recently trained in them.
For hiring managers, this points to a talent pool that will broaden over time, as more professionals add AI capability to a data engineering, analytics or data science background. It also makes it worth looking closely at how those new skills have been applied. Our consultants explore whether a candidate’s AI experience comes from production work, structured training or both. That way, you can weigh transferable potential alongside proven delivery and decide where upskilling your own team fits alongside external hiring.
Specialisms
We cover key tech specialisms within financial services that help fuel transformation and growth within business.
Skills
Our specialist recruitment consultants work with key skills areas and niche fintech communities, building their knowledge, network and experience to deliver excellent service to candidates and clients.
