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Senior Data Scientist, Fraud Prevention
Отличная позиция в известном финтехе с современным стеком (GCP, Vertex AI) и сильной инженерной культурой. Привлекательный соцпакет, включая опционы и гибкий график, делает вакансию очень конкурентной.
Сложность вакансии
Роль требует глубоких знаний в области Deep Learning и графовых моделей, а также опыта работы с высоконагруженными системами в реальном времени. Высокая планка ответственности за предотвращение фрода в финтехе добавляет сложности.
Анализ зарплаты
Зарплата в объявлении не указана, но для позиции Senior Data Scientist в Лиссабоне рыночный диапазон составляет от 55 000 до 85 000 евро в год. Учитывая статус GoCardless как глобальной компании, предложение, скорее всего, находится на верхней границе рынка или включает значительный пакет акций.
Сопроводительное письмо
I am writing to express my strong interest in the Senior Data Scientist position within the Fraud Prevention team at GoCardless. With a robust background in deploying machine learning models into production environments and a deep interest in graph-based detection and behavioral modeling, I am excited by the opportunity to protect your global payment network. My experience aligns perfectly with your stack, as I have extensive experience using Python, SQL, and Google Cloud Platform to build scalable, high-performance solutions.
In my previous roles, I have successfully managed the full model lifecycle, from initial feature engineering to real-time monitoring. I am particularly drawn to GoCardless's commitment to using AI-powered solutions like Vertex AI to reduce fraud while maintaining a seamless experience for legitimate customers. I am a strong advocate for clean code and technical mentorship, and I look forward to contributing to the technical roadmap and fostering a collaborative environment within your Lisbon-based team.
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Откликнитесь в gocardless уже сейчас
Присоединяйтесь к GoCardless, чтобы создавать передовые ML-системы для борьбы с финансовыми преступлениями в глобальном масштабе!
Описание вакансии
About Us at GoCardless
GoCardless is a global bank payment company. Over 100,000 businesses, from start-ups to household names, use GoCardless to collect and send payments through direct debit, real-time payments and open banking.
GoCardless processes US$130bn+ of payments annually, across 30+ countries; helping customers collect and send both recurring and one-off payments, without the chasing, stress or expensive fees. We use AI-powered solutions to improve payment success and reduce fraud. And, with open banking connectivity to over 2,500 banks, we help our customers make faster, more informed decisions.
We are headquartered in the UK with offices in London and Leeds, and additional locations in Australia, France, Ireland, Latvia, Portugal and the United States.
At GoCardless, we're all about supporting you! We’re committed to making our hiring process inclusive and accessible. If you need extra support or adjustments, reach out to your Talent Partner — we’re here to help!
And remember: we don’t expect you to meet every single requirement. If you’re excited by this role, we encourage you to apply!
The role
Fraud prevention is an ever-evolving puzzle, making it one of the most rewarding and impactful areas in fintech. At GoCardless, our Fraud Prevention team develops the intelligent systems that protect our platform, our merchants, and their customers.
As a Senior Data Scientist, you will guide the design and delivery of models that operate in real time across our global payment network. You’ll work at the intersection of machine learning, graph-based detection, and behavioural modelling to stay ahead of changing patterns turning research into production systems that make a tangible difference in the fight against financial crime.
You’ll collaborate closely with Engineers, Fraud Analysts, and Product Managers to help shape the technical roadmap as we expand into new markets. Our stack is centred around Google Cloud Platform and Vertex AI, using Python, SQL, and BigQuery to build and support high-performance models at scale.
What you'll do
- Contribute to the full model lifecycle, from initial discovery and feature engineering through to production, experimentation, and continuous monitoring.
- Design and refine real-time ML systems that reduce false positives and ensure a smooth, secure experience for legitimate customers.
- Foster a responsive feedback loop, adapting models as patterns evolve to ensure our defences remain robust and effective.
- Help shape the technical direction of fraud prevention ML at GoCardless, exploring new approaches as we grow globally.
- Support the team’s growth through technical mentorship, knowledge sharing, and a commitment to high-quality code.
What excites you
- Solving complex, dynamic problems: Developing systems that can adapt and respond to changing environments.
- End-to-end ownership: Seeing a project through from a conceptual business need to a live, impactful solution.
- Strategic influence: Contributing to the ML roadmap and helping determine what we build next, rather than just how we build it.
- Holistic Data Science: Engaging in deep-dive analysis, prototyping, and live experimentation.
- Modern Infrastructure: Building production-grade models on GCP and Vertex AI, with the flexibility to explore advanced architectures.
What excites us
- You hold a degree (or PhD) in a STEM discipline, or bring equivalent depth through commercial experience.
- You have hands-on experience with modelling approaches such as deep learning, graph-based methods, or sequence models. While fintech experience is great, we value transferable skills from fields like cybersecurity or risk modelling.
- You have a track record of deploying models in production that create measurable value.
- You can translate complex ML concepts into clear, practical solutions for stakeholders across the business.
- You enjoy writing clean code and helping the team thrive through thoughtful reviews and shared learning.
The Good Stuff!
- Wellbeing: Dedicated support and medical cover to keep you healthy.
- Work Away Scheme: Work from anywhere for up to 90 days in any 12-month period.
- Hybrid Working: Our hybrid model offers flexibility, with in-office days determined by your team.
- Equity: All permanently employed GeeCees get equity to share in our success.
- Parental leave: Tailored leave to support your life's great adventure.
- Time off: Annual holiday leave based on your location, supplemented by 3 volunteer days and 4 wellness days.
Life at GoCardless
We're an organisation defined by our values; We start with why before we begin any project, to ensure it’s aligned with our mission. We make it happen, working with urgency and taking personal accountability for getting things done. We act with integrity, always. We care deeply about what we do and we know it's essential that we be humble whilst we do it. Our Values form part of the GoCardless DNA, and are used to not only help us nurture and develop our culture, but to deliver impactful work that will help us to achieve our vision.
Diversity & Inclusion
We’re building the payment network of the future, and to achieve our goal, we need a diverse team with a range of perspectives and experiences. As of July 2024, here’s where we stand:
- 45% identify as women
- 23% identify as Black, Asian, Mixed, or Other
- 10% identify as LGBTQIA+
- 9% identify as neurodiverse
- 2% identify as disabled
If you want to learn more, you can read about our Employee Resource Groups and objectives here as well as our latestD&I Report
Sustainability at GoCardless
We’re committed to reducing our environmental impact and leaving a sustainable world for future generations. As co-founders of the Tech Zero coalition, we’re working towards a climate-positive future. Check out our sustainabilityaction plan here.
Find out more about Life at GoCardless via X,Instagram andLinkedIn.
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Навыки
- Python
- SQL
- Google Cloud Platform
- Vertex AI
- BigQuery
- Machine Learning
- Deep Learning
- Graph Theory
- Feature Engineering
Возможные вопросы на собеседовании
Проверка опыта работы с основным стеком компании.
Расскажите о вашем опыте развертывания моделей в Google Cloud Platform с использованием Vertex AI. С какими основными трудностями вы сталкивались?
Оценка профильных знаний в области борьбы с мошенничеством.
Как бы вы спроектировали систему обнаружения фрода на основе графовых методов для сети платежей в реальном времени?
Проверка умения балансировать между безопасностью и удобством пользователя.
Как вы подходите к оптимизации модели для снижения уровня ложноположительных срабатываний (False Positives), не жертвуя при этом точностью обнаружения фрода?
Оценка навыков работы с данными и фичами.
Какие поведенческие признаки (behavioral features) вы считаете наиболее важными при моделировании транзакционного фрода?
Проверка лидерских качеств и менторства.
Опишите случай, когда вам приходилось объяснять сложную концепцию машинного обучения стейкхолдерам, не имеющим технического образования. Каков был результат?
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