4 of 4 Logistic Regression Jobs

postgraduate preferred) in a quantitative discipline such as Computer Science, Statistics, Mathematics, Economics, or similar Working knowledge of statistical and machine learning methods (e.g. logistic regression, gradient boosting, random forests, clustering, NLP Proficient in Python and/or R, with strong experience in data quality, model development ...

apply statistical methods to transform large amounts of structured and unstructured data into predictive models to drive product management. Apply machine learning techniques (including logistic regression, decision trees, random forests, and k-means) and experimentation frameworks to design and develop explanatory and predictive models from consumer behavior data … data scientist, data analyst, or other closely related position. Related experience must include: Econometrics Experience with machine learning techniques including logistics regression, decision trees, random forests, and k-means Experience developing in-house tools to analyze and process large data sets Experience with Python, SQL and experimentation frameworks. Position ...

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 ...