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Machine Learning Scientist

Оценка ИИ

Adyen — топовый финтех-единорог с сильной инженерной культурой и работой над реальными задачами для гигантов вроде Meta и Uber. Высокий балл за интересные технологические вызовы (Causal Inference, Real-time ML) и прозрачный процесс найма, несмотря на отсутствие удаленки.


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Сложность вакансии

ЛегкоСложно
Оценка ИИ

Высокая сложность обусловлена требованием 'Engineering-First' подхода: кандидат должен не только знать алгоритмы, но и самостоятельно управлять инфраструктурой (Kubernetes, Docker) и работать с Big Data (Spark Streaming). Также требуется экспертиза в специфической области причинно-следственного вывода (Causal Inference).

Анализ зарплаты

Медиана100 000 €
Рынок80 000 € – 120 000 €
Оценка ИИ

Зарплата в объявлении не указана, но для позиции уровня Senior/Middle+ в Амстердаме рыночный диапазон составляет €85,000 - €115,000. Adyen обычно предлагает конкурентоспособные пакеты, включающие опционы (RSU), что может значительно увеличить общий доход.

Сопроводительное письмо

I am writing to express my strong interest in the Machine Learning Scientist position at Adyen. With over 4 years of experience in developing production-ready machine learning models and a deep focus on anomaly detection and time-series analysis, I am excited by the opportunity to contribute to the Proactive Diagnostics initiative. My background in building end-to-end ML lifecycles—from feature engineering in PySpark to deploying services on Kubernetes—aligns perfectly with your 'Engineering-First' mindset.

What particularly draws me to Adyen is your commitment to moving beyond simple detection toward automated root-cause analysis using Causal Inference. In my previous roles, I have consistently prioritized business impact and reliability, ensuring that complex statistical signals are translated into actionable insights. I am eager to bring my expertise in Big Data frameworks and my proactive approach to the Insights team to help maintain Adyen's position as the gold standard in financial technology.

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Откликнитесь в adyen уже сейчас

Присоединяйтесь к Adyen в Амстердаме, чтобы создавать ML-решения мирового уровня для лидеров рынка!

Описание вакансии

This is Adyen

Adyen provides payments, data, and financial products in a single solution for customers like Meta, Uber, H&M, and Microsoft - making us the financial technology platform of choice. At Adyen, everything we do is engineered for ambition.

For our teams, we create an environment with opportunities for our people to succeed, backed by the culture and support to ensure they are enabled to truly own their careers. We are motivated individuals who tackle unique technical challenges at scale and solve them as a team. Together, we deliver innovative and ethical solutions that help businesses achieve their ambitions faster.

Machine Learning Scientist

At Adyen, we are the financial technology platform of choice for the world’s leading companies. The Insights team is at the core of this platform, providing the world’s largest merchants with the data and analytics they need to optimize their payment performance. Within this, our Proactive Diagnostics initiative acts as a proactive guard for merchant revenue, closing the loop between detecting an anomaly and providing a clear path to rectification.

We operate at the intersection of Big Data and actionable intelligence. By leveraging Adyen’s global payment flow, we apply advanced statistical models and Causal Inference to not only detect performance drops but to explain the "why" behind them. We are looking for a Machine Learning Engineer to help us architect the next generation of our diagnostic engine.

In this role, you will:

  • Build – Design and scale production-ready ML models to identify anomalies across millions of traffic permutations. You will own the end-to-end lifecycle, from feature engineering within our Big Data ecosystem (Spark/SparkStreaming) to building the internal infrastructure—such as our FastAPI-based labeling service on Kubernetes—needed to generate high-quality ground truth for supervised learning.
  • Discover – Move beyond simple detection to build automated root-cause analysis. You will develop logic that translates complex statistical signals into actionable recommendations, helping merchants understand exactly how to optimize their setup.
  • Scale – Transition our diagnostic capabilities from daily reporting to near-real-time velocity. You will build the observability layer to track model drift and integrity, ensuring our signals remain the "Gold Standard" for the industry.
  • Collaborate – Work at the heart of a product-driven team. You will sit close to our users, gathering continuous feedback to ensure our technical solutions solve real-world business friction and drive product adoption.

Who You Are:

  • You have 4+ years of experience as a Machine Learning Engineer or Data Scientist (Anomaly Detection, Time-Series, or Signal Processing).
  • You have an Engineering-First mindset. You treat ML code like production code and are comfortable managing your own deployments and infrastructure.
  • You are proficient in Python and Big Data frameworks (PySpark, Airflow, Hadoop, Kafka).
  • Experience with SparkStreaming/Flink, Docker and Kubernetes is a plus.
  • You have a strong interest in Causal Inference - you want to prove why something happened, not just that it happened.
  • You are a pragmatic problem solver. You prioritize business impact and reliability over model complexity, choosing the right tool for the job to ship solutions that work today.
  • You are proactively taking the lead in projects, from ideation to deployment. You have experience working with a wide range of stakeholders and can clearly communicate complex outcomes to a wide range of audiences.
  • You can confidently work in a product team with demanding stakeholders, are able to communicate effectively and have the ability to drive the team’s roadmap, alongside the product and engineering leadership of the team.

Our Diversity, Equity and Inclusion commitments

Our unique approach is a product of our diverse perspectives. This diversity of backgrounds and cultures is essential in helping us maintain our momentum. Our business and technical challenges are unique, and we need as many different voices as possible to join us in solving them - voices like yours. No matter who you are or where you’re from, we welcome you to be your true self at Adyen.

Studies show that women and members of underrepresented communities apply for jobs only if they meet 100% of the qualifications. Does this sound like you? If so, Adyen encourages you to reconsider and apply. We look forward to your application!

What’s next?

Ensuring a smooth and enjoyable candidate experience is critical for us. We aim to get back to you regarding your application within 5 business days. Our interview process tends to take about 4 weeks to complete, but may fluctuate depending on the role. Learn more about our hiring process here. Don’t be afraid to let us know if you need more flexibility.

This role is based out of our Amsterdam office. We are an office-first company and value in-person collaboration; we do not offer remote-only roles.

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Навыки

  • Python
  • PySpark
  • Airflow
  • Hadoop
  • Kafka
  • Spark Streaming
  • Flink
  • Docker
  • Kubernetes
  • FastAPI
  • Causal Inference
  • Anomaly Detection
  • Time Series

Возможные вопросы на собеседовании

Вакансия делает упор на 'Engineering-First' и владение инфраструктурой.

Расскажите о вашем опыте развертывания ML-моделей в Kubernetes: с какими трудностями вы сталкивались при масштабировании?

Команда занимается Proactive Diagnostics, где важно понимать причины аномалий.

Как бы вы подошли к задаче поиска первопричины (root-cause analysis) резкого падения конверсии платежей, используя методы Causal Inference?

Работа предполагает обработку миллионов транзакций в реальном времени.

В чем основные различия при проектировании признаков (feature engineering) для пакетной обработки в Spark и для потоковой обработки в Spark Streaming?

Adyen ценит прагматизм и влияние на бизнес.

Опишите случай, когда вам пришлось выбрать более простую модель вместо сложной. Как это решение повлияло на бизнес-метрики и поддержку системы?

Роль требует отслеживания дрейфа моделей.

Какие метрики и инструменты вы используете для мониторинга model drift и обеспечения целостности данных в продакшене?

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