Senior Data Scientist

At Satori Analytics, we aim to change the world one algorithm at a time by bringing clarity to global brands through Data & AI. From cloud-based ecosystems for fintech to predictive models for airlines, our cutting-edge solutions cover the entire data lifecycle—from ingestion to AI applications.

As a fast-growing scale-up, our team of 100+ tech specialists—including Data Engineers, Data Scientists, and more—delivers innovative analytics solutions across industries like FMCG, retail, manufacturing and FSI. Join us as we lead the data revolution in South-Eastern Europe and beyond!

We're looking for a Senior Data Scientist to design, build, and operate the scalable data platforms behind a global payments & financial-services leader's marketing services products. You'll enable campaign measurement, customer insights, reporting, and AI/ML initiatives — partnering with Product, Engineering, Data Science, and Business stakeholders to turn complex data challenges into secure, reliable, high-quality solutions at enterprise scale.

• Define business problems and translate them into data science solutions.

• Collect, clean, and analyze large datasets.
• Develop and optimize machine learning and statistical models.
• Communicate analytical findings and recommendations to stakeholders.
• Work with stakeholders to understand business problems and contribute to how they're solved with ML/Optimization.
• Analyze messy data from various data sources.
• Build robust Supervised/Unsupervised ML pipelines, with the support and guidance from senior team members.
• Maintain and improve existing ML solutions.

• A STEM Bachelor’s degree from a reputable university - Master’s degree in Statistics/Data Science/Operations Research or equivalent is a strong plus.
• Work Experience: 3+ years of experience in Data Science/ML roles.  
• Programming: Deep understanding of algorithmic thinking. Ability to distinguish between good (clean, efficient and maintainable) and bad python code and gradual adoption of best practices. Understanding of SQL is a plus.
• ML / Data Science skills: Good understanding of ML with exposure to at least two real world projects involving Supervised, Unsupervised or Reinforcement Learning/Optimization. The ability to explore large, complex datasets to understand them before modeling (EDA, plotting).
• Statistics: have a solid grasp of basic probability and statistical theory (random variables & distributions, conditional probability & Bayes’ Theorem, expected value & measures of dispersion, hypothesis testing & p-values etc.)
• Business acumen: be able to translate business requirements into clean modeling practices and understand that the simplest solution that works is usually the best one.
• Learning & Adaptability: be curious and willing to learn new methods, frameworks and techniques
• Tools, Libraries & Frameworks: pandas/polars for data manipulation, cleaning & exploration, scikit-learn for classical machine learning workflows, xgboost/lightgbm/catboost for high-performance gradient boosting ensembles. Knowledge of git for version controlling, UV for python version and package management or pytorch for building, training and deploying DNNs is a plus.

Nice to haves:

• Experience with FastAPI and Docker for building ML-centric applications
• Working cloud knowledge (preferably Azure)
• Familiarity with MLOps tracking platforms like MLflow