Data Scientist

Role Overview

Frame business problems as data science problems and define success metrics together with stakeholders. Perform exploratory data analysis, feature engineering and statistical analysis on large datasets using Databricks, PySpark and Spark SQL.

What You Will Do

Develop, train and validate ML models (classification, regression, forecasting, clustering, NLP) with rigorous experimentation and evaluation. Deploy models to production via batch and real-time serving (Databricks Model Serving), applying CI/CD and MLOps best practices.

Why It Might Be a Fit

Collaborate with data engineers, analysts and architects to ensure reliable, well-governed data for modelling. Communicate insights and model results to technical and business audiences, including dashboards and apps built on Databricks Apps.

Requirements

• 4–8 years of data science experience
• Hands-on experience with Databricks, PySpark, MLflow, Databricks Feature Store, Unity Catalog and Model Serving
• Strong Python (pandas, scikit-learn) and PySpark; strong SQL
• Solid foundation in statistics, probability, hypothesis testing and experiment design (A/B testing)
• Proven experience taking ML models from prototype to production (MLOps)
• Strong communication skills and the ability to work with international clients; very good English
• Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, Data Science or a related field

Tagged as: Data Analysis, Forecasting, Python, SQL