Data Scientist

- 5+ years building and validating models against real decisions, with Python as your primary language, plus the engineering fundamentals that go with it: testing, code review, Git, containers, and version control for data and experiments.

- Depth in machine learning and deep learning: supervised and unsupervised methods, CNNs and Transformers, with hands-on PyTorch or an equivalent framework, including training, custom modules, and optimization.

- Expert command of pandas, NumPy and scikit-learn.

- Strong foundation in statistics and experimental design: baselines, holdouts, significance, and the ability to state how confident you are and why.

- Fine-tuning or adaptation of pretrained models, and a clear view of when fine-tuning is the wrong answer.

- Error analysis as a discipline. You can separate a data problem from a label problem, a method problem and a framing problem, rather than reaching for a larger model.

- Cloud experience with AWS, GCP or Azure and their AI services such as Amazon SageMaker, Google Vertex AI or Azure Machine Learning.

- Active use of AI-assisted coding tools such as Claude Code, Cursor, or GitHub Copilot in real delivery work.

- Clear written and spoken English, C1 or above, and the confidence to explain a technical trade-off directly to a client.

- Bachelor's or Master's degree in Computer Science, Data Science, or a related field, or equivalent professional experience.

- Evaluation of LLM systems: test-set design, model-as-judge or equivalent scoring, and benchmark selection you can defend.

- Open-weight fine-tuning and adaptation at depth: LoRA, QLoRA, PEFT, and the cost and latency trade-offs involved.

- Multimodal work covering vision, speech, or document understanding alongside text.

- Causal inference, uplift modeling, or forecasting at scale.

- Experiment tracking and MLOps tooling: MLflow, Weights & Biases, Kubeflow or similar.

- Delivery under a compliance regime such as SOC 2 or HIPAA.

- Contributions to open-source AI libraries, published research or technical writing, or active participation in the AI community.