13 of 13 Model Validation Jobs in Manchester

actionable insights for stakeholders. Collaborate with cross-functional teams including marketing, merchandising, and supply chain. Mentor and support junior data scientists and analysts. Promote best practices in data science, model validation, and experimentation (e.g., A/B testing). Key Requirements: Proven experience (5+ years) in a data science role, preferably in retail or e-commerce sectors. Strong More ❯

learning, statistical, optimisation and econometric methods to business problems. You'll contribute to the research and implementation of new approaches to address complex problems and perform data analysis and model validation. You'll also have the opportunity to present results to both internal teams and clients. What you'll be working on Developing innovative methods to exploit dunnhumby's … world class Price & Promotions capabilities Supporting our high-performing Price & Promotions experts by implementing new capabilities with significant business impact Performing exploratory data analysis and model validation What we expect from you Master's degree/PhD in Computer Science, Machine Learning, Applied Statistics, Physics, Engineering or related field Strong mathematical and statistical skills Experience with Python, Spark More ❯

be given to candidates with a strong educational background and relevant certifications. Skills: Strong foundation in statistics and programming (R/Python). Experience with data preparation, visualisation, and model building. Knowledge of big data platforms (Hadoop, Spark) and SQL/NoSQL databases. Experience: 3+ years of experience as a Data Scientist or in a related role. Typical Responsibilities … and collecting the data required Manipulating, transforming, and cleaning the data A data scientist must deal with data anomalies such as missing values, outliers, unbalanced data, and data normalisation. Model building: This stage is the core of the data science execution, where different algorithms are used to train the data and the best algorithm is selected. A data scientist … should know: Multiple modelling techniques Model validation and selection techniques A data scientist must understand the use of different methodologies to gain insights from the data and translate those insights into business value. Model deployment: An ML model is valuable when it’s integrated into an existing production environment and used to make business decisions. Deploying More ❯

problems or opportunities. AI Strategy: Contribute to the organisation's AI strategy by identifying opportunities for leveraging AI technologies to drive innovation, improve business processes, and enhance decision-making. Model Development and Evaluation: Contribute to the development, deployment, and evaluation of AI models and to the deployment and evaluation of off the shelf AI models. Collaboration and Stakeholder Management … with organisational goals. Prototyping, developing, and deploying machine learning applications into production. Contributing to our machine learning enabled, business-facing applications. Contributing effective, high quality code to our codebase. Model validation and model testing of production models. Presenting findings to senior internal and external stakeholders in written reports and presentations. This role is for you if: Python … for API and Model development (Machine learning frameworks and tooling e.g. Sklearn) and (Deep learning frameworks such as Pytorch and Tensorflow). Understanding of machine learning techniques. Experience with data manipulation libraries (e.g. Pandas, Spark, SQL). Git for version control. Cloud experience (we use Azure/GCP/AWS). Skills we'd also like to hear about More ❯

Senior Machine Learning Operation Engineers to join our Data Function. You'll play a significant role within our Data Function, working on the design and implementation of machine learning model engineering frameworks, solutions, and best practices. You'll be technically proficient in machine learning and its applications; you'll demonstrate an understanding of data management and show a keen … Engineering, Architecture, and Software Development to ensure efficient operation and use of Data Science models and will facilitate the full life cycle of machine learning models from data ingestion, model development, testing, validation, deployment, to monitoring and retraining of models within different environments. If you've a strong understanding of Microsoft Azure, fluency in data science coding, and … skills Powering the business with the right tools What's involved: You'll contribute to the design and implementation of Machine Learning Engineering standards and frameworks. You'll support model development, with an emphasis on auditability, versioning, and data security. You'll implement automated data science model testing and validation. You'll assist in the optimisation of deployed More ❯

assist the ML team. Help design, implement, and refine ML algorithms and tools. Collaborate with cross-disciplinary teams to integrate ML models into game development. Conduct experiments to improve model performance. Help train, retrain, and maintain ML systems for game development. Participate in meetings to support updates and provide insights. Requirements: BSc in Computer Science, Mathematics, or related field More ❯

reports to communicate findings and predictions to stakeholders, including good presentation skills. Collaboration: Work closely with commercial, operational, and technical teams to understand business challenges and deliver actionable insights. Model Evaluation & Maintenance: Continuously monitor, validate, and refine predictive models to ensure accuracy and relevance. Essential Skills & Experience Develop and implement predictive models to forecast key metrics such as sales More ❯

the hospitality and transportation spacce. You’ll be at the forefront of developing forecasting models, and supporting pricing strategies. This is a hands-on role where you'll own model development end-to-end—from design and testing to deployment and monitoring. Key Responsibilities Lead efforts to improve the performance and scalability of deployed forecasting models. Design and build … internal ML and forecasting libraries. Contribute to product planning by translating business objectives into measurable results. Champion best practices in model development, validation, and inference pipelines. Stay current with state-of-the-art techniques in AI and ML and apply them effectively. Work closely with technical and non technical stakeholders. What You’ll Bring PhD is highly preferred More ❯

will support the requirements of the business. Liaise with the IT team and data scientists to strive for greater functionality in our data systems. Establish efficient, automated processes for model development, validation, implementation and documentation. The Successful Applicant The successful Data Engineer should have: Proficiency in Big Data Modelling, ETL and Data warehousing. Proficient in SQL Snowflake Tableau More ❯

and insightful visualisations and reports to communicate findings and predictions to stakeholders. Collaboration: Work closely with commercial, operational, and technical teams to understand business challenges and deliver actionable insights. Model Evaluation & Maintenance: Continuously monitor, validate, and refine predictive models to ensure accuracy and relevance. Essential Skills & Experience: Preference for ‘R’ programming language but other machine learning languages/tools More ❯

modelling environments Supporting technical leads to independently implement changes to the SAS based Stress Testing Engines and associated analytical tools Focusing on continuous improvement through addressing of the Open Model validation actions and by identification of model performance issues, addressing oversight actions and by embedding learnings and evolving business and regulatory requirements Ensuring effective design and implementation … for obtaining insights and feedback for the management of the credit stress testing models Assisting the operational/delivery arm of the credit stress testing team in relation to model usage. We need you to have Good Knowledge of Climate risk model methodologies and stress testing frameworks across a range of Retail/Business credit portfolios with a … good understanding of model usage Strong model development experience in stress testing, ideally using SAS and R Effective Climate Risk Modelling skills across physical risk and transition risk A strong understanding of climate-related regulations and reporting standards (e.g. TCFD, ESG reporting) Excellent analytical ability to solve complex problems, with a keen attention to detail Brilliant communication skills More ❯

About the Role Location: United Kingdom, Manchester Remote vs. Office: Hybrid (Remote/Office) Company: Siemens Energy Limited Organization: Grid Technologies Business Unit: Grid Solutions Employment Type: Full-time Experience Level: Not specified A Snapshot of Your Day: As a More ❯

entire lifecycle of ML projects from conception to deployment and monitoring. Guide the team in building, training, and deploying models. Ensure best practices in data preparation, feature engineering, and model validation. Establish workflows for deployment, monitoring, and scaling of models. Qualifications Proven experience as a Machine Learning Engineer, with leadership experience in deploying models in production. Experience with classical More ❯