4 of 4 Logistic Regression Jobs

quality. Familiarity with advanced patterns such as GraphRAG and agentic RAG to reduce hallucination and improve factual grounding. Technical Proficiency Machine learning algorithms: linear regression, logistic regression, decision trees, random forests, support vector machines, neural networks Data science tools: NumPy, SciPy, Pandas, Matplotlib, TensorFlow, Keras Cloud computing ...

Applied Machine Learning : Strong background in feature engineering, tabular machine learning, calibrated scoring systems, and production use of models such as gradient‐boosted trees, logistic regression, and related approaches. Content‐Aware Recommendation Systems : Experience combining embeddings, semantic similarity, collaborative signals, and content understanding to build recommendation systems that ...

consultants on analytic client engagements involving descriptive, predictive, and prescriptive analysis through the consumer lending portfolio lifecycle, leveraging a variety of techniques (e.g., segmentation, logistic regression, etc). You will deliver analytic insights and recommendations in succinct and compelling presentations for internal and external customers. You will design ...

contract with strong likelihood of extension (or potential permanent conversion). Key Responsibilities Develop and deploy ML models for loan/deal scoring (classification, regression, ranking) Analyse financial, credit, and time-series data to extract predictive signals Build and benchmark models such as: Gradient boosting (XGBoost, LightGBM, CatBoost) Random … Forests Logistic/linear regression Design LLM-powered workflows for: Document understanding (loan docs, credit memos, financials) Risk factor extraction Deal-level reasoning and recommendations Combine structured ML outputs with LLM-based reasoning to deliver explainable scores Work closely with stakeholders to translate credit policy into data-driven ...