Overview
As a Data Scientist, you will build and scale machine learning solutions to support product intelligence and data-informed decision-making.
What you'll do
- Design and develop end-to-end ML solutions from data exploration to deployment.
- Collaborate with engineers, analysts, and product teams to integrate ML models into applications.
- Implement scalable ML pipelines using Databricks, PySpark, and Delta Lake.
- Run controlled experiments such as A/B tests, uplift modelling, and causal inference.
- Operationalise models via CI/CD and MLOps practices including monitoring and governance.
- Monitor production systems for drift, performance issues, and anomalies using explainability and fairness techniques.
What you'll need
- Extensive hands-on experience applying machine learning and statistical modelling in production or product environments.
- Understanding of ML techniques spanning traditional models to deep learning architectures.
- Experience designing scalable ML pipelines and automating workflows with MLOps tools.
- Preferred experience in Python, including Scikit-learn, Autogluoone, PyTorch or TensorFlow, and PySpark MLlib.
- Familiarity with retrieval-augmented generation (RAG) and fine-tuning large language models.
- Proficiency in SQL and distributed data frameworks with experience in feature engineering at scale.
Nice to have
- Familiarity with real-time ML applications like online learning, streaming inference, or live recommendations.
- Exposure to forecasting, anomaly detection, or probabilistic modelling in production systems.
- Experience contributing to open-source projects, writing technical blogs, or presenting at conferences.
- Interest in continuous learning and keeping up with cutting-edge AI research such as foundation models and self-supervised learning.
Details
- Work flexibility: hybrid model with 2-3 days in-office.
Read the full description and apply on the company’s own careers page.
