IT Services and IT Consulting

spotixx GmbH

51-200

A laptop and the software you need, provided by the employer.

Company laptop

An employer-supported pension scheme to build up retirement savings.

Company pension plan

Adapt your start and end times around your life and workload.

Flexible working hours

Free coffee, tea, water and soft drinks at the office.

Free beverages

Work from home for part of your working week.

Home Office

A budget or equipment to set up a proper workspace at home.

Home Office Equipment

Vouchers and discounts for shops, brands and everyday purchases.

Vouchers and discounts

 Travel costs for your commute or work trips are reimbursed.

Travel expenses compensation

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Teamwork

<p>spotixx is a Frankfurt-based European FinTech building technology for the regulated financial sector. Founded in 2019, we are a 60+ people team with deep, established relationships across the German and wider European banking sector, built on years of fraud investigation and insights work delivered inside major banks.</p>
<p>We are bootstrapped and set our own strategy and our own policies, without the compromises of chasing investor timelines. The market is moving toward us: European regulators are opening the door to cross-institutional financial crime intelligence, and we are scaling the team rapidly to meet strong inbound demand.</p>
<h2>Tasks</h2>
<p>We are looking for a <strong>hands-on Data Scientist</strong> who is passionate about applying <strong>machine learning to real-world financial crime problems</strong>. You enjoy working with complex transactional data, take ownership of models from idea to production, and care about building solutions that are not only accurate but also robust, explainable, and scalable. You are comfortable working in a fast-growing scale-up and collaborating closely with engineers and product teams.</p>
<p><strong>Your responsibilities</strong></p>
<ul>
<li>Develop and improve ML models for fraud detection</li>
<li>Work on automated rule discovery and optimization for fraud detection</li>
<li>Explore transactional and behavioral data to identify suspicious patterns</li>
<li>Own feature engineering, model training, validation, and monitoring</li>
<li>Take models from prototype to production</li>
<li>Communicate results clearly to technical and business stakeholders</li>
</ul>
<h2>Requirements</h2>
<p><strong>Your profile</strong></p>
<ul>
<li>Degree in Data Science, Computer Science, Statistics, Mathematics, or a related field</li>
<li>2–3 years of hands-on experience in machine learning and applied data science, ideally in fraud detection, banking, or financial services</li>
<li>Strong proficiency in Python and SQL</li>
<li>Deep understanding of classification and anomaly detection algorithms, with the ability to select, tune, and evaluate models using appropriate metrics and validation strategies</li>
<li>End-to-end ownership: from exploring high-dimensional data and building models to visualizing results, deploying to cloud platforms (GCP, Azure, or AWS), and managing the full model lifecycle</li>
<li>Experience with LLMs and agentic workflows</li>
<li>Familiarity with model explainability tools such as SHAP and model cards</li>
<li>Structured, self-directed working style and strong communication skills</li>
<li>Fluent in English; professional working proficiency in German, including client-facing technical discussions</li>
</ul>
<h2>Benefits</h2>
<p><strong>What we offer</strong></p>
<ul>
<li>Attractive compensation</li>
<li>Flexible hybrid work model, allowing you to split your time between our office in Frankfurt and working from home</li>
<li>High ownership and real impact in a growing fintech</li>
<li>Flat structures and short paths from idea to action</li>
<li>A collaborative, international team culture</li>
</ul>
<p>We welcome applications from all qualified people, as diversity enriches us. Your perspective matters: regardless of gender, age, origin, religion, sexual orientation, or disability. Applicants with disabilities will be given preference when equally qualified.</p>

Data Scientist (f/x/m)

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