22 of 22 Linear Regression Jobs in London

The Role: Data Cleaning and Preparation: Collect, clean, and prepare large media datasets from various sources (CRM, ad servers, audience panels) for analysis. Statistical Analysis: Utilise econometric techniques like regression analysis, time series modelling, and panel data analysis to identify relationships between media spend and business outcomes. Model Validation and Interpretation: Evaluate the accuracy and robustness of models, interpret … Data Science: Proficient in programming languages like Python, R, and SQL including data manipulation, data imputation, statistical modelling, and visualisation libraries. Econometrics Background Useful: Expertise in statistical methods like linear regression, generalised linear models, panel data analysis, and time series forecasting. Media Industry Knowledge: Understanding of media landscape, ad formats, audience measurement, and industry KPIs. Communication Skills More ❯

The Role: Data Cleaning and Preparation: Collect, clean, and prepare large media datasets from various sources (CRM, ad servers, audience panels) for analysis. Statistical Analysis: Utilise econometric techniques like regression analysis, time series modelling, and panel data analysis to identify relationships between media spend and business outcomes. Model Validation and Interpretation: Evaluate the accuracy and robustness of models, interpret … Data Science: Proficient in programming languages like Python, R, and SQL including data manipulation, data imputation, statistical modelling, and visualisation libraries. Econometrics Background Useful: Expertise in statistical methods like linear regression, generalised linear models, panel data analysis, and time series forecasting. Media Industry Knowledge: Understanding of media landscape, ad formats, audience measurement, and industry KPIs. Communication Skills More ❯

Knowledge of Bayesian methods, neural networks (preferably PyTorch). Strong statistical background, especially Bayesian reasoning. Experience with causal inference models. Proficiency in various modeling techniques: gradient boosting, neural networks, linear regression. Expertise in Python, capable of developing REST services or UI components. Understanding of Kafka, Docker, and related technologies. Ability to own projects end-to-end. Data-driven, structured More ❯

massive datasets, effective exploratory data analysis, and model building using industry standard time Series Forecasting techniques like ARIMA, ARIMAX, Holt Winter and formulate ensemble model. Proficiency in both Supervised (Linear/Logistic Regression) and Unsupervised algorithms (k means clustering, Principal Component Analysis, Market Basket analysis). Experience in solving optimization problems like inventory and network optimization. Should have … hands on experience in Linear Programming. Work closely with internal stakeholders like the business teams, engineering teams and partner teams and align them with respect to your focus area. Detail-oriented and must have an aptitude for solving unstructured problems. You should work in a self-directed environment, own tasks and drive them to completion. Excellent business and communication More ❯

Experience: 10+ years Job Description: Must have good experience in implementing machine learning models such as Prophet, ARIMA, SARIMA, XGBoost, ElasticNet, Ridge, Lasso, Random Forest, and Linear Regression on time-series data. Proficient in using Python ML packages such as scikit-learn, sktime, and darts. Must have strong expertise in key techniques for time series feature engineering, including More ❯

Experience: 10+ years Job Description: Must have good experience in implementing machine learning models such as Prophet, ARIMA, SARIMA, XGBoost, ElasticNet, Ridge, Lasso, Random Forest, and Linear Regression on time-series data. Proficient in using Python ML packages such as scikit-learn, sktime, and darts. Must have strong expertise in key techniques for time series feature engineering, including More ❯

sensor and assay performance data Implement multivariate computational models (e.g., Principal Component Analysis (PCA), Partial Least Squares (PLS)) to identify key measurement variables within complex electrochemical datasets. Develop non-linear regression models to improve the accuracy of immunoassay data analysis. Apply machine learning techniques, including Random Forest and neural networks, to classify sample types based on electrochemical measurements … in computational modelling, data analysis, and machine learning techniques. Proficiency in Python, R, MATLAB, or other statistical programming languages. Knowledge of multivariate analysis techniques (e.g., PCA, PLS) and non-linear regression models. Experience developing predictive machine learning algorithms (e.g., Random Forest, Neural Networks). Proficiency in SQL (preferably MySQL) and database management for engineering data storage Experience working 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 ❯

Direct message the job poster from Harnham Building Data Science and Machine Learning Teams in the UK | The Talent Driving The Data and AI Revolution Data Scientist - (Forecasting/Regression) Remote (UK-Based) Up to £45,000 The Company This UK-based start-up has grown rapidly in just two years. With a team of approximately 30 people, they … you’ll develop and refine KPI prediction models, working with real-world data to generate actionable insights. Clean and process data to ensure accuracy and usability. Build and maintain linear regression models for KPI tracking. Access APIs and integrate software tools to improve workflows. Collaborate with the revenue team to produce insightful reports. Support internal product development by … They're Looking For 1-2 years of experience in data science or a related field, or a Master’s degree. Strong Python skills (essential), SQL, and experience with linear regression/statistical modeling. Experience with web scraping, machine learning, or dashboarding is a plus. A background in finance or familiarity with financial data is advantageous but not More ❯

statistical analyses leading to the understanding of the structure of data sets. Proven and demonstrable experience with at least three of the following machine learning algorithms: neural networks, logistic regression, non-linear regression, random forests, decision trees, support vector machines, linear/non-linear optimization. Experience working with Java and Python, and strong understanding of More ❯

quantitative researcher, quantitative analyst or another relevant role Degree in Applied Mathematics, Computer Science, Financial Engineering, Technology or Engineering Knowledge of probability theory, inferential statistics, machine learning, Bayesian statistics, linear algebra, and numerical methods Experience with statistical and machine learning models, such as regression-based models (e.g., logistic regression, linear regression, negative binomial regression More ❯

statistical analyses leading to the understanding of the structure of data sets. Proven and demonstrable experience with at least three of the following machine learning algorithms: neural networks, logistic regression, non-linear regression, random forests, decision trees, support vector machines, linear/non-linear optimization. Experience working with Java and Python, and strong understanding of More ❯

statistical analyses leading to the understanding of the structure of data sets. Proven and demonstrable experience with at least three of the following machine learning algorithms: neural networks, logistic regression, non-linear regression, random forests, decision trees, support vector machines, linear/non-linear optimization. Experience working with Java and Python, and strong understanding of More ❯

data scientist, quantitative researcher, quantitative analyst or another relevant roleDegree in Applied Mathematics, Computer Science, Financial Engineering, Technology or EngineeringKnowledge of probability theory, inferential statistics, machine learning, Bayesian statistics, linear algebra, and numerical methodsExperience with statistical and machine learning models, such as regression-based models (e.g., logistic regression, linear regression, negative binomial regression), tree 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 ❯

Azure Machine Learning Integrated with Power BI Get familiar with Machine Learning Workspace Predict Diabetes Score using Linear Regression Prerequisites- Requires Azure Subscription for creating Automated ML Workspace Install Python on your system Understanding of Linear Regression algorithm Let’s first have a quick overview on Linear Regression and then we will deep dive … into the process of creating Automated ML model and this model will get integrated into Power BI. Linear regression is a linear approximation of a relationship between two or more variables. Regressor model are highly used by data scientist to make prediction over continuous numerical values. Basically, the process of linear regression is mentioned as … as per your choice but it should follow some objective of making predictions Design Machine Learning Model that works on the dataset Make predictions on the dataset (based on Linear regression algorithm There is dependent variable which is called Y being predicted and independent variable X1, X2, X3..........Xn . Here x is a predictor and Y is function More ❯

Their insights empower investors to make informed decisions by providing performance data ahead of public reports. Responsibilities: Clean and process data to ensure accuracy and usability. Build and maintain linear regression models for KPI tracking. Access APIs and integrate software tools to enhance workflows. Collaborate with the revenue team to generate insightful reports. Support internal product development by … improving data pipelines and analysis. Requirements: 1-2 years in data science or a related field, or a Master’s degree. Strong Python programming (essential), SQL, and linear regression/statistical modelling. Experience with web scraping, machine learning, or dashboarding is a plus. A background in finance or exposure to financial data is advantageous but not required. How More ❯

a centralised team, collaborating with Data Scientists, Python developers, Actuaries and senior insurance experts to drive innovation in pricing and risk assessment by developing GLM, Gradient Boost, Bayesian and Linear Regression models for pricing models. What You’ll Be Doing: Enhancing actuarial models with advanced statistical and machine learning techniques. Developing and optimising pricing models using R, tidyverse 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 ❯

analysis and metrics into actionable insights for the advertiser and both technical and non-technical stakeholders - In depth understanding of advanced statistical techniques and methodologies as well as simple linear regression analysis, multivariate regression analysis and model building - Advanced in SQL with the ability to write complex queries from scratch from multiple tables - Fluency with Python (or More ❯

engineering on large datasets, conduct exploratory data analysis, and build models using time series forecasting techniques such as ARIMA, ARIMAX, Holt Winter, and ensemble methods. Apply supervised learning algorithms (linear/logistic regression) and unsupervised algorithms (k-means, PCA, market basket analysis). Solve optimization problems related to inventory and network optimization, with hands-on experience in linear … CS, Math, ML, Stats, Operations Research). Proficiency in Python, R, or similar scripting languages; experience with SQL, MySQL, and databases. Expertise in machine learning, statistics, and optimization, including linear programming. Experience with statistical measures such as hypothesis testing, confidence intervals, and error analysis. Excellent communication skills for technical and non-technical audiences. Preferred additional skills include experience with More ❯

analysis and metrics into actionable insights for the advertiser and both technical and non-technical stakeholders - In depth understanding of advanced statistical techniques and methodologies as well as simple linear regression analysis, multivariate regression analysis and model building - Advanced in SQL ability to write complex queries from scratch from multiple tables - Fluency with Python (or R) - ability More ❯