2 of 2 Permanent Exploratory Data Analysis Jobs in Glasgow

Social network you want to login/join with: Data Scientist – London or Glasgow | Hybrid The Data Gals is working with a top-tier, award-winning organisation at the forefront of digital transformation within professional services. With a strong reputation for innovation and cross-functional collaboration, they are growing their data and analytics team to support next … generation solutions. We’re looking for a Data Scientist who’s passionate about innovation, experimentation, and using data to drive strategic decisions. You’ll join a dynamic environment that blends subject matter expertise, digital tools, and modern methodologies to design smarter, scalable, data-driven services. Location Open to candidates based near Glasgow (up to £55k) or London … up to £65k) Key Responsibilities Build, test, and deploy data models and analytical solutions that support transformation and digital service delivery. Collaborate with cross-functional teams to uncover patterns, surface insights, and shape future services. Apply statistical techniques, machine learning models, and exploratory data analysis to diverse, often complex datasets. Communicate findings through visual storytelling, dashboards More ❯

This is a new position for a Data Scientist with a global, data-driven company with cutting-edge technology who leverage data to serve as a true market differentiator. The focus of this role is to deliver data science solutions which impact in areas such as the effectiveness of advertising and the optimisation of dynamic creatives. … lead on the life cycle, high-impact projects Scope and stage projects to include detailed milestones and delivery schedules. Use SQL and/or Python (JupyterNotebooks) to preparedata, perform exploratory data analysis, evaluate different modeling approaches. Engage in problem-solving, fault-finding, addressing issuesin the data or approaches as they arise. Investigate the data and … experience of: Working in a Data Scientist role in a high value commercial environment. Requirements gathering and idea generation Defining problems (and criteria for success), Data wrangling, EDA, modeling and interpreting results Providing relevant insights Advanced statistical and analytical techniques and concepts sampling methods, Regression Properties of distributions Weighting sample-based data Statistical tests proper usage Real More ❯