5 of 5 Permanent LightGBM Jobs in London

and actionable way Mentor junior data scientists and contribute to the evolution of data science standards and practices Key Skills & Technologies Technical: Strong in Python (pandas, scikit-learn, XGBoost, LightGBM, etc.) Proficient in SQL for complex customer data extraction and manipulation Experience with customer analytics techniques: segmentation, RFM analysis, clustering, time-series, A/B testing, uplift modeling Familiarity with More ❯

environments. 5–7 years of experience in a credit-related data science or decision science role Proficiency in Python and experience with libraries such as scikit-learn, XGBoost, or LightGBM Strong SQL skills for data extraction and transformation Experience working with transactional (e.g., Open Banking) and bureau data (e.g., Experian, Equifax) Expertise in feature engineering, handling class imbalance, and evaluating More ❯

environments. 5–7 years of experience in a credit-related data science or decision science role Proficiency in Python and experience with libraries such as scikit-learn, XGBoost, or LightGBM Strong SQL skills for data extraction and transformation Experience working with transactional (e.g., Open Banking) and bureau data (e.g., Experian, Equifax) Expertise in feature engineering, handling class imbalance, and evaluating More ❯

mentorship. Have good communication skills. Nice to have Experience deploying LLMs and agent-based systems Our technology stack Python and associated ML/DS libraries (scikit-learn, numpy, pandas, LightGBM, LangChain/LangGraph, TensorFlow, etc ) PySpark AWS cloud infrastructure: EMR, ECS, ECR, Athena, etc. MLOps: Terraform, Docker, Spacelift, Airflow, MLFlow Monitoring: New Relic CI/CD: Jenkins, Github Actions More More ❯

and successfully. Youre confident in owning the process independently, from problem formulation to monitoring impact in production Technical Proficiency: Strong Python and experience with ML libraries (e.g., Scikitlearn, Pandas, LightGBM), plus working knowledge of cloud infrastructure and data tools (Snowflake, DBT, Omni) Structured Thinking: You use a clear and repeatable process to approach ML problems, including problem formulation, KPI definition More ❯