22 of 22 Permanent Linear Regression Jobs

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 ❯

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 ❯

Ability to assess and articulate regulatory implications and expectations of distinct modeling efforts. Advanced experience with the concepts and technologies associated with classical supervised modeling for prediction such as linear/logistic regression, discriminant analysis, support vector machines, decision trees, forest models, etc. Advanced experience with the concepts and technologies associated with unsupervised modeling such as k-means More ❯

Ability to assess and articulate regulatory implications and expectations of distinct modeling efforts. Advanced experience with the concepts and technologies associated with classical supervised modeling for prediction such as linear/logistic regression, discriminant analysis, support vector machines, decision trees, forest models, etc. Advanced experience with the concepts and technologies associated with unsupervised modeling such as k-means 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 ❯

Python, Javascript, R, SQL, Scala, etc. • Proficiency with data mining, mathematics, and statistical analysis demonstrated with hands-on academic and project experience conducting statistical analysis, testing, and modeling using regression analysis, linear regression, predictive modeling, chi-squared, clustering (K-means, density, etc) • Experience using tools such as tools such as MATLAB, SAS, and InfoSphere • Experience with a More ❯

as SQL, Python, R, Spark, Hadoop etc.) commonly associated with delivery of Data Science solutions. Experience in developing and reviewing modeling solutions based on broad range of techniques - e.g., linear and logistic regressions, time series methods, survival analysis, support vector machines, neural networks, decision trees, random forests, gradient-boosting methods, deep learning, k-means and other clustering methods, simulation More ❯

as SQL, Python, R, Spark, Hadoop etc.) commonly associated with delivery of Data Science solutions. Experience in developing and reviewing modeling solutions based on broad range of techniques - e.g., linear and logistic regressions, time series methods, survival analysis, support vector machines, neural networks, decision trees, random forests, gradient-boosting methods, deep learning, k-means and other clustering methods, simulation 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 ❯

or above) in Computer Science, Data Science, AI or similar Solid grounding in statistics – understanding of Bayesian and frequentist approaches, distributions, etc. Knowledge of core ML techniques like linear/logistic regression, random forests, time series models, etc. Familiarity with SQL and Python Bonus points if you have explored prompt engineering or large language models A self-starter More ❯

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 ~30 people, they are scaling quickly, supported by recent funding and a strong growth pipeline. They specialise in predictive analytics, tracking KPIs for a wide range of companies and … role in developing and refining KPI prediction models, working with real-world data to create 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 enhance workflows. Collaborate with the revenue team to generate insightful reports. Support internal product development by … pipelines and analysis. What They're Looking For 1-2 years in data science or a related field, or a Master’s degree. Strong Python programming (essential), SQL, and linear regression/statistical modeling. Experience with web scraping, machine learning, or dashboarding is a plus. A background in finance or exposure to financial data is advantageous but not 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 ❯

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 ❯

deliverables. You will: Lead and mentor a team of data scientists in building predictive models. Oversee data cleaning, feature engineering, and model development pipelines. Build and maintain robust, scalable linear regression and statistical models for KPI forecasting. Drive improvements in internal tooling and API integrations. Collaborate closely with leadership, engineering, and the revenue team to translate business needs … 5+ years’ experience in data science or a closely related field. Proven leadership experience — mentoring or managing junior data scientists. Expert Python programming skills (essential). Strong grasp of linear regression, statistical modeling, and data processing best practices. Proficient in SQL (MySQL preferred). Experience with web scraping, machine learning techniques, and dashboarding tools is a bonus. Familiarity More ❯

science! THE ROLE As the Senior Analyst you will utilise your strong SQL & Python experience across a range of ststatical analysis projects, including customer segmentation, marketing measurement, forecast modelling, linear regression & synthetic control methodology YOUR SKILLS AND EXPERIENCE Strong Python Strong SQL Statistical Analysis THE BENEFITS £65,000-£75,000 Fully Remote Working (In The UK) HOW TO More ❯

You'll Need: Proven success with profitable trading strategies. Strong programming skills in C Python in a Linux environment. Working knowledge of forecasting and data mining techniques, such as linear and non-linear regression analysis, neural networks, or support vector machines. Strong experience developing statistical models in a trading environment. Proven success working with large data sets More ❯

quantitative market research . You should: Be comfortable using and interpreting data to draw insights Have a basic understanding of key statistical techniques such as: Conjoint analysis MaxDiff Segmentation Linear or logistic regression Significance testing Be confident using Excel , PowerPoint , and Word Be open to learning tools such as: SPSS , Q , Sawtooth , Displayr , Crunch.io (experience with any of More ❯

quantitative market research . You should: Be comfortable using and interpreting data to draw insights Have a basic understanding of key statistical techniques such as: Conjoint analysis MaxDiff Segmentation Linear or logistic regression Significance testing Be confident using Excel , PowerPoint , and Word Be open to learning tools such as: SPSS , Q , Sawtooth , Displayr , Crunch.io (experience with any of More ❯

to tools and trends that allow you to make an impact with data. You have a grasp of statistical methods and their practical application, such as significance testing or linear regression. You have some experience using SQL and you are excited about developing your technical skills. It’s a bonus if you have used Python or R for data More ❯

to tools and trends that allow you to make an impact with data. You have a grasp of statistical methods and their practical application, such as significance testing or linear regression. You have some experience using SQL and you are excited about developing your technical skills. It’s a bonus if you have used Python or R for data More ❯