Data Scientist (m/f/d)

About Buynomics: 

“What will customers buy?” is the crucial question underlying all commercial decisions in all companies. Buynomics has developed Virtual Shopper technology, which answers this question 100x faster and more precisely than every other solution. This will end the common practice of gut feeling. Our goal is to enable companies to make data-driven, transparent, and customer-centric commercial decisions. For that, we build the operating system for commercial decisions that provides companies with a single source of truth to answer all their market-facing questions including pricing, product offers, and promotions. 

Buynomics is the next-generation SaaS company in pricing and product optimization. It employs large-scale simulation technologies to digitize customers and predict their purchase behavior - in any environment. Our clients use Buynomics to make data-driven decisions about their current and new products, overall portfolio structure, pricing, promotions, trade terms, and more!

Job Summary:

We are looking for an experienced Data Scientist to join our Machine Learning Applications Team. We offer an exciting opportunity to work in a young team with a modern tech stack with the chance to shape crucial processes and take on responsibility early on.

In this role, you will play a key role in improving the statistical methods that sit at the heart of our product.

Responsibilities:

• Research, develop, and maintain high-impact production features that take the accuracy of our models, the time-to-value of our data pipeline, and our user-facing app to the next level, using state-of-the-art methods from economics, statistical learning, optimization, and machine learning
• Be a reference point for data science and software engineering excellence in the team, frequently raising the bar by example and by mentoring more junior team members
• Cooperate closely with commercial counterparts to understand business problems and customer needs, and break them down into high-impact incremental deliverables
• Work closely with other technical teams on delivery

Requirements:

• 4+ years of experience as a Data Scientist, Machine Learning Engineer, Modeller
• 2+ years of experience delivering high-performance data-science software, experience from a high-performance context (e.g. consulting, early stage startup) is a plus
• MSc or PhD in Quantitative Economics, Statistics, Mathematics or a similar field from a leading university, with deep theoretical and applied knowledge of statistics and mathematics
• Ability to formalize business requirements into mathematical models
• Advanced knowledge of python and its libraries (e.g. Pandas, NumPy, PyTorch etc)
• Strong conceptual abilities with a good understanding of business needs
• Eagerness to drive value and have impact from “day 1” including a hands-on mentality to actively help yourself and others
• Excellent communication and collaboration skills

What we offer:

• A vibrant, international, and diverse work environment
• Flexible work options: remote within the EU with occasional visits to our Cologne HQ or Barcelona office or a hybrid setup in one of these locations
• A dedicated learning budget to support your professional growth and development
• Local language lessons (Germany and Spain)
• Health and fitness benefits (Germany and Spain)
• Personal and professional growth opportunities
• Regular company events

To ensure a smooth and efficient process and that no applications are overlooked, please apply directly to the role. While we'd love to respond to everyone who reaches out, applying ensures that your application is properly tracked and considered.

Buynomics is proud to be an equal opportunity employer. This means that we don’t discriminate based on race or ethnic origin, color, the language(s) you speak, where you (or your parents) are from, or whether or not you consider yourself to have a disability. Neither will your age, gender, gender identity, sexual orientation, religion, beliefs, or political opinions play a part in your application with us. We’re a diverse team in so many ways, and we love it that way.