About the role
Requirements:
- 1-5 years in data science, ML or applied AI, including at least one model moved beyond a proof of concept.
- Strong foundations. Python, SQL, pandas, NumPy, scikit-learn, statistics, feature engineering, imbalanced learning and model evaluation.
- Modern model toolkit. XGBoost, LightGBM or CatBoost, plus PyTorch or TensorFlow for relevant deep-learning, NLP or computer-vision use cases.
- Production mindset. Comfort with APIs or batch services, testing, Git, Docker and cloud/MLOps fundamentals; AWS/SageMaker, MLflow or Spark is a plus.
- Clear thinking. You can explain assumptions, trade-offs and limitations simply, and collaborate well with domain experts, product managers and engineers.
What will make you stand out:
- End-to-end proof. A model you personally moved from problem framing and validation into a real pilot or production workflow.
- Experimental rigour. A clear explanation of baselines, leakage prevention, metric choice, threshold trade-offs and segment-level error analysis.
- Ownership under ambiguity. Examples of turning an unclear business ask into a measurable ML problem and influencing stakeholders with evidence.