Principal Consultant - Data Scientist Lead

вчера

ChinaWorldwideLeadHybrid

google cloudmachine learningfraud detectionml pipelinesmodel validationcost optimization

Lead hands-on design, diagnostics and optimisation of fraud ML models and Google Cloud infrastructure to improve cost, performance and reliability in fraud detection.

Обязанности

As a Data Scientist Lead at , 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.

Будет плюсом

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.

Условия

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.

Другое

Location: Hong Kong (Hybrid) | Practice Area : Data & Analytics | Type: Permanent
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.
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.
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 , 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.