Your Role as Data Scientist
What makes this role stand out:
- You’re closest to the data and analytics from exploration and cleaning, through modeling, to generating business insights.
- Your tools include classical and non-linear ML models (regression, XGBoost, LightGBM, CatBoost) and libraries such as pandas and scikit-learn.
- You create analyses and visualizations that directly support business decisions.
Why it’s worth it:
- You have a real impact on client strategies and decisions, your models and analyses don’t end up in a drawer.
- Projects are diverse from EDA and feature engineering, to predictive modeling, time-series analysis, data visualization, and dashboard development.
- You get room to grow, whether into deeper data analytics and business consulting, or towards AI engineering (working closely with MLEs and SEs).
A few project examples:
- Training multimodal LLMs for drug discovery.
- Building AI voicebots that double conversion rates.
- Creating a GenAI solution for a leading US legal company together with the OpenAI team.
- Running GenAI on edge devices with cloud-level performance.
All of this in a setup that feels like an AI-driven software house: remote-first, flexible, and packed with specialists who are open to sharing knowledge and experimenting with the newest tech.
The ideal candidate:
- Hss a minimum of 4 years of experience in Data Science, delivering end-to-end, data-driven solutions.
- Is proficient in classical ML techniques (linear regression, feature selection methods, predictive modeling, time series) as well as non-linear methods (gradient boosting, random forest, XGBoost, LightGBM, CatBoost).
- Programs fluently in Python and uses libraries such as NumPy, pandas, scikit-learn.
- Can effectively manage and analyze data using SQL.
- Creates clear and engaging visualizations (matplotlib, seaborn, plotly).
- Can translate data into actionable business insights and recommendations.
- Stands out with strong communication skills and the ability to explain complex concepts in a simple way.
- Bonus points for experience in data engineering and cloud platforms (AWS, GCP, Azure), as well as knowledge of dashboarding tools (Tableau, Power BI, Dash) and experience in NLP or leveraging LLMs/Generative AI.