Principal Consultant - Data Scientist Lead
Capco
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Interviews for scientist roles keep coming back to Python, Machine learning, SQL, C++. Practise those questions before you sit with Capco.
Questions you are likely to be asked
- Why do you want to join Capco?
- What is your experience with GCP? Tell me one thing you learned the hard way.
- How did you know your model was actually good, and not just good on your test set?
- Tell me about a time the data was messy or wrong. What did you do?
- How would you explain your model's result to someone who is not technical?
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Practise the Principal Consultant - Data Scientist Lead at Capco interview free →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.
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Listed on greenhouse · posted 2026-08-28. ApplySarthi collects openings and links to application pages; the role is advertised by Capco, not by us.