Data Scientist (f/m/d)

Hey there, future data wizard! ️ Tired of corporate buzzwords and "AI" that's more PowerPoint than Python? Do you believe the best tool for the job isn't always the most hyped one? Are you someone who loves to dig deep, understand how things really work, and build solutions from the ground up? If you just screamed "YES!", then we might have the perfect playground for you.

At smartclip, we're not about chasing trends; we're about solving real, complex problems with data. We're looking for a curious and pragmatic (Junior) Data Scientist to join our passionate team. Forget maintaining legacy code - you'll be diving headfirst into fresh challenges, turning trillions of data points into meaningful impact . You'll enjoy the freedom to tackle problems your way , at your pace - with a smart and supportive team at your side. We'll tell you what to solve - the how is up to you. You'll never be alone though: we collaborate, not micromanage.

Activities

Is your idea of fun wrangling messy data, debugging ML pipelines, and running experiments that actually tell you something? Do you know that correlation causation, and that pandas aren't just cute animals? If your heart beats faster for code, queries, and questions, keep reading. At smartclip, we're on the hunt for a Junior Data Scientist who's less into dashboards for the sake of dashboards, and more into uncovering what really matters behind the metrics.

• Explore terabytes of real-world data, not toy datasets
• Work on questions that don't have answers on StackOverflow (yet)
• Build and improve data products and ML models with a team of domain experts
• Have the freedom to use the right tool for the right job, not just the fashionable one

Requirements

Your Toolbelt

• 3+ years of hands-on experience in Data Science
• You're comfortable working with large-scale, messy datasets - even the kind that don't fit into memory
• For you state-of-the-art-methods are not just the methods everybody use . You are not afraid to search for answers in scientific literature.
• Strong mathematical intuition - you don't just apply models; you want to understand why they work
• You follow best practices by default , and know when it's time to question them

Your Tools

• Python - comfy with pandas, scikit-learn & friends
• Data Science basics - from feature engineering to model validation
• SQL - joins, aggregations, subqueries? No problem
• Docker & Linux - smooth setup and deployment
• AI/ML - real models, not just slide decks
• Git - because version control is second nature

Bonus Skills (nice to have):

Apache Spark / Kubernetes / Prefect, Airflow or similar / Torch, VLLM, or LLM-related tooling / pydantic.ai or similar / Shell scripting

Team

We believe in flat hierarchies and fast decision-making processes.

Application Process

1. Chat with Sarah (Senior via telephone call
2. Interview via google meet with your potential leader(s)
3. Interview either in person or via google meet with your potential future team
4. Feedback from the Team and Sarah on a short notice