SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Satori Analytics is a fast-growing scale-up of 100+ tech specialists delivering innovative analytics and AI solutions across industries including FMCG, retail, manufacturing, and financial services. The company provides cloud-based ecosystems and predictive models covering the entire data lifecycle from ingestion to AI applications.
You will join as a Data Scientist to design, build, and operate scalable data platforms for a global payments and financial-services leader's marketing services products. Your work will enable campaign measurement, customer insights, reporting, and AI/ML initiatives. You'll partner with Product, Engineering, Data Science, and Business stakeholders to transform complex data challenges into secure, reliable, high-quality solutions at enterprise scale.
Day-to-day responsibilities include:
- Working with stakeholders to understand business problems and contribute to ML/optimization solutions
- Analyzing messy data from various sources
- Building robust Supervised/Unsupervised ML pipelines with guidance from senior team members
- Maintaining and improving existing ML solutions
The role offers a flexible hybrid model based in the modern Athens office with the option to work remotely from anywhere in the European Economic Area (EU, Switzerland) or UK for up to 6 weeks per year. Benefits include competitive salary, private health insurance, learning and development budget for certifications and courses from top tech partners (Microsoft, AWS, Salesforce, Databricks), clear career growth opportunities, and a collaborative team environment.
REQUIREMENTS:
- Education: STEM Bachelor's degree from a reputable university; Master's degree in Statistics, Data Science, or Operations Research is a strong plus
- Work Experience: 1–3 years in Data Science/ML roles
- Programming: Deep understanding of algorithmic thinking and ability to write clean, efficient, maintainable Python code. SQL understanding 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. Ability to explore large, complex datasets through EDA and plotting
- Statistics: Solid grasp of basic probability and statistical theory (random variables, distributions, conditional probability, Bayes' Theorem, expected value, measures of dispersion, hypothesis testing, p-values)
- Business Acumen: Ability to translate business requirements into clean modeling practices; understanding that the simplest working solution is usually best
- Learning & Adaptability: Curiosity and willingness to learn new methods, frameworks, and techniques
- Tools & Libraries: Proficiency with pandas/polars for data manipulation and exploration, scikit-learn for classical ML workflows, xgboost/lightgbm/catboost for gradient boosting. Knowledge of git for version control is expected; uv for Python package management and PyTorch for DNNs are a plus
Nice to have: Experience with FastAPI and Docker for ML-centric applications, cloud knowledge (preferably Azure), familiarity with MLOps tracking platforms like MLflow.
