6 of 6 Remote Linear Regression Jobs in England

Python Proficiency: Strong coding skills using libraries like pandas, NumPy, scikit-learn, PyTorch, and Hugging Face Transformers. Writing clean, modular, and testable code. Traditional Machine Learning Models: Experience with regression (linear, ridge), classification (logistic regression, decision trees, random forests), clustering (k-means, DBSCAN), and time-series forecasting (ARIMA, Prophet). Model evaluation, tuning, and deployment. Business Requirement More ❯

across the platform to support commercial growth and marketing optimisation. Deliver pre- and post-campaign analysis, segmentation work, and ongoing performance tracking. Build and deploy statistical models such as linear regression, clustering, forecasting, and advanced A/B tests. Work closely with ML Engineers, commercial teams, and senior stakeholders to translate insight into business action. Support marketing measurement … science, or advanced analytical modelling. Hands-on experience using Python for statistical analysis and modelling. Advanced SQL skills for data extraction and manipulation. Experience applying statistical methods such as linear regression, KMM, forecasting models, and experimentation. Strong communication skills with the ability to simplify complex analysis for business audiences. Degree in Mathematics, Statistics, Economics or related quantitative field. … to Mohammed Buhariwala at Harnham using the Apply link on this page or connect directly to find out more. Keywords Insight Analyst, Customer Analytics, Data Science, SQL, Python, Forecasting, Linear Regression, Marketing Analytics, Churn, Segmentation, Experimentation, A/B Testing, Membership, Subscription, Data Modelling, Hybrid, Remote. More ❯

Science (2:1 and above). Strong numerical background with a knowledge of key statistical principals e.g. Bayesian and frequentist statistics, probability distributions. Fundamental understanding of ML algorithms e.g. linear and logistic regression, random forest, neural networks, time series models. Experience with multiple programming languages, with a preference for SQL and Python. Familiarity with large language models and More ❯

Science (2:1 and above). Strong numerical background with a knowledge of key statistical principals e.g. Bayesian and frequentist statistics, probability distributions. Fundamental understanding of ML algorithms e.g. linear and logistic regression, random forest, neural networks, time series models. Experience with multiple programming languages, with a preference for SQL and Python. Familiarity with large language models and More ❯

Markdown documentation for building, testing, and deploying. Cloud : Leverage cloud infrastructure (e.g., AWS EC2), databases, and configuration with markup files for remote management and deployment. Model : Implement models (e.g., linear regression, gradient boosting) with training/testing datasets, cross-validation, performance visualisation, and use hosted APIs; explore techniques like time-series forecasting, clustering, or Bayesian inference. Orchestration and More ❯

data using SQL (Google BigQuery) and Python. Strong foundational knowledge with practical experience in designing and analyzing A/B tests. Well-versed in various analysis techniques, such as linear and logistic regression, significance testing, and statistical modeling. Previous experience in the IAM domain is a strong plus but not a must. This is an excellent opportunity to More ❯