(Senior) Data Scientist

Why join us

Brack.Alltron is one of Switzerland's leading e-commerce companies, with around 1,300 employees and over a billion in annual revenue. We're a market leader in both B2C and B2B, a combination that's almost unique in Europe, backed by one of the continent's most modern logistics setups: highly automated, scalable, and built for further growth. Together, we shape the commerce of tomorrow, and we're scaling ambitiously, growing our engineering and data teams across Switzerland and Poland.

With us, you'll find real space to shape things in an environment driven by ambition, ownership, and heart. We celebrate wins together and learn from mistakes. We value direct, honest feedback because we all want to keep learning. That's how we build a culture of trust, knowledge sharing, and the courage to actually move things forward.

Your Mission

As a Data Scientist, you turn business questions into data-driven solutions end-to-end. From problem framing through prototyping to bringing models in production you own it all. You work in close collaboration with business owners, product teams, and our Data Platform team.

You take a rapid prototyping approach to validate value hypotheses quickly and honestly, and you bring successful prototypes into production on our cloud platform. You combine scientific rigor with pragmatism. We're not looking for a researcher, but for someone who delivers impact.

Key expertise

• Scientific rigor & product mindset: Apply structured methods without losing pragmatism. Work in cross-functional teams along build–measure–learn cycles.

• Modeling craft: Strong foundation in statistics and classical ML (regression, classification, clustering, SVM, …), plus more sophisticated techniques where the use case calls for them.

• Production & MLOps mindset: Care about how a model lives in production (versioning, deployment, monitoring, drift detection) and not just how it performs in a notebook.

• Data fluency: Work confidently with messy real-world data, understand their structure, quality, and limitations.

• End-to-end ownership: Drive use cases from problem framing to production, taking ideas from "interesting question" to "running in production" on your own initiative.

• Rapid prototyping: Design lean experiments that test value hypotheses with minimal effort and clear success criteria.

• Collaboration: Partner closely with engineers, product teams, and business stakeholders.

• Problem shaping: Translate ambiguous business questions into well defined ML problems while challenging the business and trimming wishful features along the way.

• Honest evaluation: After prototyping and validation, call it as it is. A well-founded "kill it" is just as valuable as a "ship it."

Your impact

• Validate business value with rapid prototypes that give clear, honest answers about whether a use case is worth scaling.

• Bring models into production together with our Data Platform team.

• Own the full loop from framing the question with stakeholders to monitoring the model after deployment.

• Shape our data science practice as we grow the team. Your standards, your code, your approach becomes part of how we work.

• Contribute to our open feedback culture, where transparency, constructive challenge, and mutual support help the team get better.

• Drive cross-functional collaboration with Software Engineers, Analysts and Data Platform Engineering to keep our data products consistent.

What you bring

• A degree in a quantitative field (Statistics, Mathematics, Computer Science, Data Science, Physics, Econometrics, or comparable).

• 4+ years of hands-on data science experience with a clear end-to-end track record.

• Demonstrated experience in rapid prototyping and validating business value hypotheses.

• Strong foundation in statistics and classical ML plus comfort with modern techniques.

• Fluency in Python and SQL and the standard toolbox (pandas, scikit-learn, statsmodels, optionally PyTorch/TensorFlow).

• Hands-on experience with modern data platforms (e.g. Databricks, Snowflake etc.), and with Tools like MLflow / Git for experiment tracking and versioning.

• A production mindset: experience with MLOps practices, model deployment, and monitoring is a clear plus.

• Excellent communication skills in English. German is a plus.

• The ability to thrive in ambiguity – you're proactive, resourceful, and ready to take ownership.

We're probably not a great match if you...

• Prefer a pure research role and aren't excited about taking your own work to production.

• Like to optimize models in isolation and would rather not engage with messy business questions.

• Need a perfectly clean data environment and rigid process to feel comfortable. Spoiler: we're not there yet.

• Prefer to receive finished requirements instead of shaping them together with the business.

• View professional development mainly as the employer's responsibility, rather than learning out of your own curiosity.

• Strictly separate work from having a good time.