Data Scientist leading data research and algorithm development for Next Level Coffee's innovative coffee products. Collaborating to improve coffee quality and personalization through data-driven insights.
Responsibilities
You support our team by leading data research and algorithm development that directly improves coffee quality and personalization
You work at the intersection of data, sensory science, and engineering to turn raw machine signals into meaningful insights and repeatable excellence in every cup
You develop and refine data models that improve consistency and flavor outcomes in espresso brewing
You are responsible for analyzing machine and sensor data to identify key variables influencing coffee quality
You take on the design and validation of hybrid modeling approaches that combine statistical methods, physics-based reasoning, and data-driven techniques
You contribute to the creation of predictive models that help detect performance drifts and optimize brewing parameters
You collaborate with coffee domain experts and engineers to translate your models into actionable insights for product and process improvement
You have delivered validated algorithmic approaches that measurably improve beverage consistency and reproducibility
You have established a systematic process for exploratory data analysis, model validation, and experiment evaluation, building on the existing data infrastructure
You have identified the most critical machine and environmental factors affecting coffee quality and quantified their impact
You are recognized as a core contributor who bridges scientific reasoning and practical application, working closely with data engineering, platform, and coffee teams
Requirements
A Master’s or PhD in Physics, Applied Mathematics, Statistics, or a related quantitative discipline with a strong theoretical foundation
7+ years of experience applying data science to complex, real-world problems, ideally in IoT, manufacturing, process optimization, or other sensor-driven systems
Proven mastery of statistical inference, causal modeling, Bayesian reasoning, and uncertainty quantification
Expertise in advanced modeling techniques, such as Bayesian optimization, Gaussian processes, probabilistic programming, and hierarchical modeling
Deep understanding of experimental design, simulation-based modeling, and system identification for complex physical or hybrid systems
Advanced proficiency in Python, with hands-on experience using scientific and probabilistic frameworks such as NumPy, SciPy, PyMC, GPyTorch, or TensorFlow Probability
Experience guiding the full data science lifecycle: from framing questions and defining success metrics to model evaluation and scientific communication
Ability to synthesize domain knowledge, collaborate with specialists, and translate scientific insights into scalable, data-informed product improvements
Benefits
Hybrid working - you work remotely, but once a month we meet for 2-4 days in Konstanz, the Alps, or Mallorca
Your home office setup tailored to your workday needs
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