Principal Consultant - Data Scientist Lead
Capco is hiring a Principal Consultant - Data Scientist Lead in Hong Kong, Hong Kong SAR China. Level rates it ; you can apply on Capco.
Level's read on this job
- Level rates this data scientist role Builds AI ●●●●: the job is building AI systems.
- The posting mentions AI safety among its tools and skills.
- Level classes it as a lead role.
- Capco has 702 other open roles on Level.
AI in this role
Data Scientist Lead
Location: Hong Kong (Hybrid) | Practice Area: Data & Analytics | Type: Permanent
The Role
As a Data Scientist Lead at Capco, you’ll help clients optimise fraud models, machine learning platforms and supporting Google Cloud infrastructure, turning detailed analysis into practical, implementation-ready recommendations.
You’ll take a hands-on approach to improving model and platform performance, reliability and run-cost across GCP infrastructure and ML pipelines. Working closely with IT infrastructure teams, data scientists and fraud stakeholders, you’ll identify opportunities to reduce compute and inference costs while maintaining effective, resilient solutions.
You’ll provide configuration-level and code-level guidance, supported by clear estimates of implementation effort, expected savings and measurable outcomes. You’ll also use approved AI tools and AI-enabled workflows where appropriate to accelerate analysis, improve insight and support delivery, while maintaining human judgement, appropriate review and responsible AI guardrails.
What You’ll Do
- GCP engineering: Lead hands-on design, diagnostics and optimisation across compute, storage and network services, implementing monitoring and observability to improve cost, performance and reliability.
- Fraud ML modelling: Support the development, validation and deployment of fraud and ML models end-to-end, identifying measurable opportunities to reduce compute requirements, accelerate inference and improve automation.
- ML and data pipelines: Optimise pipelines spanning ingest, transformation, feature creation and serving, training and inference, with a focus on cost efficiency, performance, resilience and responsible AI-enabled automation where appropriate.
- Big data and distributed processing: Develop and tune large-scale batch and streaming workloads using Spark or similar technologies, improving efficiency, stability and production readiness.
- Engineering excellence: Apply strong Python and SQL skills alongside CI/CD, automated testing, modular design and disciplined code review, using approved AI tools where appropriate to improve engineering productivity while ensuring outputs remain subject to human review and established controls.
What We’re Looking For
- Strong hands-on experience in data science, machine learning engineering or ML platform optimisation, including the ability to translate analysis into practical, implementation-ready recommendations.
- Deep experience with Google Cloud Platform and the optimisation of cloud compute, storage, networking and ML workloads for performance, reliability and cost.
- Strong Python and SQL skills, together with experience building, validating, deploying and optimising production ML models and data pipelines.
- Experience with large-scale distributed data processing technologies such as Spark and applying sound software engineering practices including CI/CD, automated testing and code review.
- An ability to collaborate effectively with infrastructure, data science and business stakeholders, with curiosity about approved AI tools and the judgement to apply AI-enabled workflows responsibly, including appropriate guardrails, review, escalation and auditability.
Bonus Points For
- Experience supporting fraud detection, financial crime or other high-volume decisioning models within financial services.
- Exposure to or implementation experience with Feedzai, including integration, performance tuning or operationalisation.
- Experience demonstrating measurable cloud or ML cost savings, including reduced compute consumption, faster inference or improved automation.
- Familiarity with production monitoring, model/platform observability and performance optimisation across complex ML environments.
- Experience applying AI, including Generative AI or Agentic AI where appropriate, to engineering or data workflows within controlled and regulated environments.
Why Join Capco
- Deliver high-impact technology solutions for Tier 1 financial institutions
- Work in a collaborative, flat, and entrepreneurial consulting culture
- Access continuous learning, training, and industry certifications
- Be part of a team shaping the future of digital financial services
- Help shape the future of digital transformation across FS & Energy.
Inclusion at Capco
We’re committed to making our recruitment process accessible and straightforward for everyone. If you need any adjustments at any stage, just let us know – we’ll be happy to help. We value each person’s unique perspective and contribution. At Capco, we believe that being yourself is your greatest strength. Our #BeYourselfAtWork culture encourages individuality and collaboration – a mindset that shapes how we work with clients and each other every day.
How we rate this
Principal Consultant - Data Scientist Lead at Capco rates 93 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Also common in data scientist jobs at this level
Pay
- Typical data scientist job
- $167k
This posting doesn't list pay. Use the typical data scientist figure when pay comes up. See data scientist jobs
Questions you could be asked
- How do you think about the risk of an AI system in this kind of role failing silently?
- How would you decide a model or AI system is ready to ship?
- Tell me about a time a model underperformed in production. How did you find out, and what did you change?
Adapt your resume
- List these exact terms on your resume: AI Safety. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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