Principal Consultant - Data Scientist Lead
The Data Scientist Lead at Capco will optimize fraud models, machine learning platforms, and Google Cloud infrastructure for financial services clients. The role involves hands-on engineering, performance tuning of ML pipelines, and implementing cost-efficient, resilient solutions using Python, SQL, and GCP.
- Compensation
- Not disclosed
- Location
- China - Hong Kong
- Work setup
- Not specified
- Seniority
- Principal
- Employment
- Full time
- Experience
- Not specified
- Category
- Data science
- Posted
Skills
Must have
- Hands-on experience in data science, machine learning engineering or ML platform optimisation
- Deep experience with Google Cloud Platform
- Strong Python and SQL skills
- Experience building, validating, deploying and optimising production ML models and data pipelines
- Experience with large-scale distributed data processing technologies such as Spark
- Experience applying software engineering practices including CI/CD, automated testing and code review
Nice to have
- Experience supporting fraud detection or financial crime models within financial services
- Exposure to or implementation experience with Feedzai
- Experience demonstrating measurable cloud or ML cost savings
- Familiarity with production monitoring and model/platform observability
- Experience applying Generative AI or Agentic AI in regulated environments
Full description
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.