Milan | Engineering | Full-time

Data
Scientist

Love always drives what we do, we’re passionate about our jobs, care about our colleagues, and are devoted to our customers. Our mission is to become the most loved European financial service provider. We’re already expanding rapidly into new countries, and we’re not done yet. We have big plans. And that’s where you come in.

Mollie is on an exciting journey. Our mission is to become the most loved Payment Service Provider in Europe. The Data Domain comprises four teams: Data Science (DS; including ML Engineering); Data Engineering, Analytics Engineering, and Data Governance. In this central Domain, we enable Mollie and its Merchants to create value from data by providing a central data platform, data models, and machine learning (ML) models.

Your Opportunity

We are looking for an experienced Data Scientist to join our growing team of Data Scientists (DSs) & Machine Learning Engineers (MLEs). Our team is set up as a central entity that provides advanced analytics capabilities across Mollie. We primarily develop predictive ML models that provide decision intelligence to our colleagues in various departments, such as Product, Operations, and Commerce. We also maintain a cloud-based ML Platform, which we use for both model development and production.

In this role, you will be focused on developing ML models for Operations, to tackle challenges in fraud, transaction monitoring, credit risk, integrity risk, and more. This is a hands-on role, where you will spend most of your time developing in Python together with our team of DSs and MLEs. You will also be interfacing directly with subject matter experts & stakeholders, e.g. to evaluate the feasibility of potential DS use cases or to explain how your model works during a Sprint Review.

This role is based at Mollie’s new Milan Hub. You will be part of a geographically-distributed team (Amsterdam/Lisbon/Milan) that is comfortable collaborating virtually & hybrid.

What you'll be doing

Develop ML models for a range of use cases in Operations. Ensure these models smoothly reach production and bring measurable value to our stakeholders
Perform efficient exploratory data analysis (EDA) and present key insights to colleagues & stakeholders
Contribute regularly to our bi-weekly DS Community of Practice (knowledge sharing sessions)
Adopt best practices and standards for the development of ML models
Collaborate closely with MLEs to prepare your code & model for production using our ML Platform
Help evaluate the feasibility of new use cases for ML in Operations

What you'll bring

1-3 years experience in data science and machine learning, including the development of models that successfully went to production
You have experience with tabular datasets and applied ML, in particular regression and classification problems using boosted decision tree algorithms
You approach complex problems in a structured way, always looking for the simplest, pragmatic solutions
You understand the technical and non-technical constraints of a business problem
You are detail-oriented but can also quickly shift priorities if required
You enjoy working collaboratively in a cross-functional & distributed team environment
You have excellent presentation skills and can communicate to a wide variety of audiences
You have a strong foundation in statistics
You have solid software engineering skills and love coding in Python
You know your way around a linux shell and are comfortable with Git for version control
You know the scikit-learn API inside and out
You are comfortable in an agile Way of Working, with Scrum or similar frameworks

Nice to have

Experience with anomaly detection algorithms
Experience with (Py)Spark to handle large datasets
Experience in the financial services industry (banking or fintech)
Experience with Google Cloud Platform Vertex AI or similar
Familiarity with DevOps & MLOps principles
Ph.D. in Machine Learning, the natural sciences, or similar

5 reasons why you should be working here
We bring open hearts and open minds
1.
We grow what we know and learn what we don’t
2.
We are driven by customer love
3.
We celebrate the journey together
4.
We know how to have fun
5.

About Engineering

We’re in the driving seat. Without us, there is no Mollie. So we’re given the freedom to explore. Make mistakes, learn, and grow. Because that’s how we’ll become the most loved financial service provider.

More about Engineering at Mollie

Our stack

Full stack

Diversity, Equity, and Inclusion

These three words tell you everything about Mollie… Driven. By. Love. Our driving force, our reason for being… to become the world’s most loved financial services provider. But not just loved by our customers… by our people too. At Mollie we embrace what makes you unique, and nurture a culture that feels like home. We celebrate diversity of people and perspectives every day and are proud to be an equal opportunity employer. We do not discriminate. Every new Mollie is hired on the basis of qualifications, merit, and business need. We bring open hearts and open minds, we don’t judge each other for our differences, we embrace the unique traits that make our products and culture stronger. There is an energy at Mollie that can’t be contained. We are proud to be here, and inspired by the problems we have left to solve. So come join the ride.

Find out more

Who you’ll be working with

Isabelle
Head of Data
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Ana
Data Scientis
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Joren
Analytics Engineer
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Anique
Senior Data Analyst
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Darragh
Senior Data Engineer
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From
Mollie
with
love

Equity plans

Noise cancelling headphones

Birthday off

Company parties

MacBook

Work from home budget

Wellbeing program

Learning and development

Supersize screen

Referral bonus

Every good relationship starts with a click. Let’s make sure this one will last.

For starters
Our Talent Acquisition team and hiring manager will review your application, and respond within 2 weeks.
The "inbetween"
If you seem like a Mollie-in-the-making, we’ll invite you to a screening call so we can learn more about each other.
Are you the one?
If all goes well, you’ll have two or more interviews with the hiring team. And if it’s a highly technical role, we’ll also assess the specific skills you’ll need.