semi-structured and unstructured data. Ability to use dimension reduction techniques (PCA, encoders etc.) Excellent familiarity with elastic net logistic regression, random forest and XGBoost ensembles to work on supervised problems with structured, tabular data. We currently use Scikit-learn, and we're open to suggestions for additional libraries. Classification more »
deploying production ML systems. - Familiarity with DevOps in a data science context (e.g., MLOps) is a plus. Tools currently being used; - Python3, Numpy, Scipy, Xgboost - CI/CD: GitHub Actions, Jenkins, Docker - MLOps: DVC, MLflow - BI: Terraform, Airflow, BigQuery - LLMs: GPT, Claude And this is what you’ll get in more »
deploying production ML systems. - Familiarity with DevOps in a data science context (e.g., MLOps) is a plus. Tools currently being used; - Python3, Numpy, Scipy, Xgboost - CI/CD: GitHub Actions, Jenkins, Docker - MLOps: DVC, MLflow - BI: Terraform, Airflow, BigQuery - LLMs: GPT, Claude And this is what you'll get in more »
Brighton, England, United Kingdom Hybrid / WFH Options
15gifts
data science tech stack Python Docker & Kubernetes AWS Cloud Deep learning frameworks - Pytorch and Tensorflow HuggingFace ecosystem [optional] Other machine learning frameworks - scikit-learn, XGboost, CatBoost etc Ability to understand and develop state-of-the-art implementations Familiarity with state-of-the-art deep learning (e.g. transformers) and reinforcement learning more »
