26 to 50 of 58 Feature Engineering Jobs in London

data scientist, with a track record of leading successful AI projects. Proficiency in AI and machine learning frameworks and programming languages (e.g., Python). Strong expertise in data preprocessing, feature engineering, and model evaluation. Excellent problem-solving and critical-thinking skills. Effective leadership, communication, and team management abilities. A passion for staying at the forefront of AI and More ❯

marketing and customer experience. What You'll Do Support the development of predictive models and data-driven solutions that solve real marketing and customer problems. Conduct exploratory data analysis, feature engineering, and data cleaning to prepare data for modelling. Write clean, well-documented Python and SQL code to support analysis and model development. Collaborate with other data scientists More ❯

improve and automate decision making Collaborate with technical and non-technical teams to understand problems, explore data, and develop effective fraud prevention tools and solutions Design and maintain robust feature engineering pipelines for modelling, working closely with analytics engineering teams Contribute to the development of end-to-end machine learning workflows and help embed models into production … heavily imbalanced datasets Excellent skills in Python and SQL Solid understanding of classification algorithms such as gradient boosting decision trees, including pros and cons of different model architectures Strong feature engineering skills and experience in transforming raw data into useful model inputs Effective communication skills and able to explain complex findings clearly to both technical and non-technical More ❯

improve and automate decision making Collaborate with technical and non-technical teams to understand problems, explore data, and develop effective fraud prevention tools and solutions Design and maintain robust feature engineering pipelines for modelling, working closely with analytics engineering teams Contribute to the development of end-to-end machine learning workflows and help embed models into production … heavily imbalanced datasets Excellent skills in Python and SQL Solid understanding of classification algorithms such as gradient boosting decision trees, including pros and cons of different model architectures Strong feature engineering skills and experience in transforming raw data into useful model inputs Effective communication skills and able to explain complex findings clearly to both technical and non-technical More ❯

and prospects' questions, based on knowledge of Acadian's processes and pertinent new research. Explore structured and unstructured datasets with a focus on data preparation, transformation, outlier detection, and feature engineering. Collaborate on the design of ESG constraints for client-driven investment solutions, help build predictive models and design interactive data applications. We're Looking for Teammates With: Bachelor More ❯

platforms using Dataproc, Dataflow, Pub/Sub, BigQuery, Cloud Spanner, and Bigtable. Define and implement real-time and batch processing pipelines for complex use cases involving streaming analytics, ML feature engineering, and automation. Act as a trusted advisor to senior technical and business stakeholders across industries (e.g., telecom, retail, financial services). Drive data quality, governance, lineage, and … security standards across enterprise data pipelines. Mentor engineering teams and lead best practice adoption across data architecture, orchestration, and DevOps tooling. Participate in technical workshops, executive briefings, and architecture reviews to evangelize GCP data capabilities. Required Qualifications Bachelor’s or Master’s degree in Computer Science, Data Engineering, or related technical field. 12+ years of experience in data … architecture and data engineering with proven skills and leadership in large-scale cloud data programs. 5+ years of advanced expertise in Google Cloud data services: Dataproc, Dataflow, Pub/Sub, BigQuery, Cloud Spanner, and Bigtable. Hands-on experience with orchestration tools like Apache Airflow or Cloud Composer. Hands-on experience with one or more of the following GCP data More ❯

platforms using Dataproc, Dataflow, Pub/Sub, BigQuery, Cloud Spanner, and Bigtable. Define and implement real-time and batch processing pipelines for complex use cases involving streaming analytics, ML feature engineering, and automation. Act as a trusted advisor to senior technical and business stakeholders across industries (e.g., telecom, retail, financial services). Drive data quality, governance, lineage, and … security standards across enterprise data pipelines. Mentor engineering teams and lead best practice adoption across data architecture, orchestration, and DevOps tooling. Participate in technical workshops, executive briefings, and architecture reviews to evangelize GCP data capabilities. Required Qualifications Bachelor’s or Master’s degree in Computer Science, Data Engineering, or related technical field. 12+ years of experience in data … architecture and data engineering with proven skills and leadership in large-scale cloud data programs. 5+ years of advanced expertise in Google Cloud data services: Dataproc, Dataflow, Pub/Sub, BigQuery, Cloud Spanner, and Bigtable. Hands-on experience with orchestration tools like Apache Airflow or Cloud Composer. Hands-on experience with one or more of the following GCP data More ❯

platforms using Dataproc, Dataflow, Pub/Sub, BigQuery, Cloud Spanner, and Bigtable. Define and implement real-time and batch processing pipelines for complex use cases involving streaming analytics, ML feature engineering, and automation. Act as a trusted advisor to senior technical and business stakeholders across industries (e.g., telecom, retail, financial services). Drive data quality, governance, lineage, and … security standards across enterprise data pipelines. Mentor engineering teams and lead best practice adoption across data architecture, orchestration, and DevOps tooling. Participate in technical workshops, executive briefings, and architecture reviews to evangelize GCP data capabilities. Required Qualifications Bachelor’s or Master’s degree in Computer Science, Data Engineering, or related technical field. 12+ years of experience in data … architecture and data engineering with proven skills and leadership in large-scale cloud data programs. 5+ years of advanced expertise in Google Cloud data services: Dataproc, Dataflow, Pub/Sub, BigQuery, Cloud Spanner, and Bigtable. Hands-on experience with orchestration tools like Apache Airflow or Cloud Composer. Hands-on experience with one or more of the following GCP data More ❯

Qualifications: PhD in a relevant discipline or Master’s plus a comparable level of experience Experience in Knowledge Graphs or Large Document Search Experience with traditional ML models and feature engineering. Strong Experience with fine tuning, modelling and deploying LLMs - experience with RAG, IR, NER etc would also be very beneficial Strong programming skills (e.g., Python) and experience with … modern ML frameworks (e.g., PyTorch, TensorFlow, LangChain). Collaborating with other Researchers, Product, Engineering and Business Stakeholders in an agile manner to demonstrate value and iterate with customer feedback. Please apply below for immediate consideration More ❯

Qualifications: PhD in a relevant discipline or Master’s plus a comparable level of experience Experience in Knowledge Graphs or Large Document Search Experience with traditional ML models and feature engineering. Strong Experience with fine tuning, modelling and deploying LLMs - experience with RAG, IR, NER etc would also be very beneficial Strong programming skills (e.g., Python) and experience with … modern ML frameworks (e.g., PyTorch, TensorFlow, LangChain). Collaborating with other Researchers, Product, Engineering and Business Stakeholders in an agile manner to demonstrate value and iterate with customer feedback. Please apply below for immediate consideration More ❯

Principal ML/AI Engineer - Python | Greenfield AI Platform (Contract) We are seeking a Principal/Senior AI/ML Engineer with a strong foundation in software engineering and Python development, to join a globally renowned organisation at the forefront of AI innovation. This is a rare chance to work on greenfield AI/ML projects, delivering transformative solutions … systems using Python and modern frameworks. Lead technical design discussions and influence system-level decisions across greenfield initiatives. Build and scale end-to-end ML pipelines, including data ingestion, feature engineering, model training, and deployment. Champion best practices in software engineering: CI/CD, testing, containerisation, version control, and code quality. Collaborate with cross-functional teams including … data scientists, platform engineers, and product stakeholders to align AI solutions with business goals. Experience Strong commercial experience in Python with a deep understanding of software engineering principles. Demonstrated expertise in building ML systems in production environments (not just notebooks or research). Proven leadership experience in either a senior IC or tech lead/managerial capacity. Familiarity with More ❯

Principal ML/AI Engineer - Python | Greenfield AI Platform (Contract) We are seeking a Principal/Senior AI/ML Engineer with a strong foundation in software engineering and Python development, to join a globally renowned organisation at the forefront of AI innovation. This is a rare chance to work on greenfield AI/ML projects, delivering transformative solutions … systems using Python and modern frameworks. Lead technical design discussions and influence system-level decisions across greenfield initiatives. Build and scale end-to-end ML pipelines, including data ingestion, feature engineering, model training, and deployment. Champion best practices in software engineering: CI/CD, testing, containerisation, version control, and code quality. Collaborate with cross-functional teams including … data scientists, platform engineers, and product stakeholders to align AI solutions with business goals. Experience Strong commercial experience in Python with a deep understanding of software engineering principles. Demonstrated expertise in building ML systems in production environments (not just notebooks or research). Proven leadership experience in either a senior IC or tech lead/managerial capacity. Familiarity with More ❯

Principal ML/AI Engineer - Python | Greenfield AI Platform (Contract) We are seeking a Principal/Senior AI/ML Engineer with a strong foundation in software engineering and Python development, to join a globally renowned organisation at the forefront of AI innovation. This is a rare chance to work on greenfield AI/ML projects, delivering transformative solutions … systems using Python and modern frameworks. Lead technical design discussions and influence system-level decisions across greenfield initiatives. Build and scale end-to-end ML pipelines, including data ingestion, feature engineering, model training, and deployment. Champion best practices in software engineering: CI/CD, testing, containerisation, version control, and code quality. Collaborate with cross-functional teams including … data scientists, platform engineers, and product stakeholders to align AI solutions with business goals. Experience Strong commercial experience in Python with a deep understanding of software engineering principles. Demonstrated expertise in building ML systems in production environments (not just notebooks or research). Proven leadership experience in either a senior IC or tech lead/managerial capacity. Familiarity with More ❯

deep experience delivering high-impact AI solutions across marketing and customer experience. What You'll Do Model & Build: Support the design and deployment of pragmatic machine learning solutions - from feature engineering in SQL to model development in Python, and deploying in production environments like AWS. Explore & Prototype: Help bring new ideas to life by quickly prototyping new models … curiosity, and clarity of thought to everything you do. Pace and impact matter here. What You'll Bring Must-Have: A degree in a STEM discipline (Computer Science, Maths, Engineering, etc.) or equivalent practical experience. 2-4 years of experience delivering DS/ML solutions in production environments - ideally in settings where you've had to wear multiple hats … e.g., startups, small teams). Fluency in Python and SQL; experience building and deploying models end-to-end, from feature engineering to performance validation. Comfort with cloud tools (AWS preferred), Git, and CI/CD pipelines. Ability to work independently and juggle priorities without getting stuck in analysis paralysis. Concise communication and documentation skills, especially under time pressure. More ❯

overall application architecture. Collaborate closely with stakeholders and researchers to support analytical and product use cases Evaluate data sources to understand the content and how it fits with our feature requirements, identifying missing or erroneous data. Architect and build data pipelines which fetch data from public and private data suppliers' APIs, S3 buckets, and web interfaces in various formats … with data from multiple sources, and perform various transformations. Create programmatically validated data schemas, as well as human-readable documentation, to specify requirements to our partners. Explore alternate data engineering technologies and solutions. Other activities as may be assigned by your manager Qualifications/Requirements: Bachelor's degree in related field, or equivalent combination of education and experience Experienced … Hardware: Mac, Linux Experience with cloud platforms (e.g., AWS, Azure). Broad understanding of Financial Services/Capital Markets/Asset Management. Experience working with geospatial data. Experience in feature engineering for Machine Learning applications. Experience with data engineering frameworks. Portfolio of past experience (e.g., demos of past work, contributions to open source, blogs, talks). Self More ❯

We are seeking a seasoned Principal Engineer to lead the design, development, and evolution of our Observability Platform , ensuring it meets the needs of our rapidly scaling systems and engineering teams. This role will also focus on leveraging Machine Learning (ML) and Artificial Intelligence (AI) to deliver advanced insights that proactively improve system health and drive down Mean Time … the development and adoption of platform capabilities to ensure system health, reliability, and performance. Establish and evolve platform standards and best practices to align with the company's overall engineering goals. Strategic Initiatives Collaborate with engineering teams to define the observability strategy, ensuring alignment with business and operational objectives. Identify and integrate the latest observability technologies, including AI … performant, and secure across all environments. Optimize data collection, processing, and storage to balance performance with cost efficiency. Define SLAs, SLOs, and SLIs for observability services to support reliability engineering practices. Continuously improve MTTD and MTTR by leveraging advanced AI/ML models for predictive analysis and automated responses. Mentorship and Collaboration Act as a mentor and technical leader More ❯

drive innovation into the product development process. You will interpret your client’s objectives, desires, and preferences to help the wider technical team understand the opportunities and apply data engineering responsibilities consistently. You will be working on an interesting range of projects that deliver to National Security customers and as such, you will have to hold the highest level … of UK Security Vetting (DV), upon application. Key Responsibilities: You will blend Data Engineering and Data Science, and will have experience that might cover a number of the tasks listed below: Data Engineering tasks Manage the implementation and development of integrations between the data warehouse and other systems. Create deployable data pipelines that are tested and robust using … Research, analyse and apply data sets using a variety of statistical and machine learning techniques. Support the analytical needs of the technical team inclusive of cleansing, mapping, statistical inferences, feature engineering and the bespoke data visualisation methods required by each project. Review the execution of software solutions and how these perform for the business and your clients, establishing More ❯

drive innovation into the product development process. You will interpret your client’s objectives, desires, and preferences to help the wider technical team understand the opportunities and apply data engineering responsibilities consistently. You will be working on an interesting range of projects that deliver to National Security customers and as such, you will have to hold the highest level … of UK Security Vetting (DV), upon application. Key Responsibilities: You will blend Data Engineering and Data Science, and will have experience that might cover a number of the tasks listed below: Data Engineering tasks Manage the implementation and development of integrations between the data warehouse and other systems. Create deployable data pipelines that are tested and robust using … Research, analyse and apply data sets using a variety of statistical and machine learning techniques. Support the analytical needs of the technical team inclusive of cleansing, mapping, statistical inferences, feature engineering and the bespoke data visualisation methods required by each project. Review the execution of software solutions and how these perform for the business and your clients, establishing More ❯

fairer credit system. The Role We're looking for an experienced MLOps Engineer for a 3-month contract to lead the development of our ML deployment, testing, monitoring, and feature engineering pipelines . You'll be responsible for establishing best practices and production-grade systems to support our machine learning workflows from training to deployment and beyond. The … pipeline using AWS , with a strong focus on SageMaker for training, deployment, and hosting. - Integrate and operationalize MLflow for model versioning, experiment tracking, and reproducibility. - Architect and implement a feature store strategy for consistent, discoverable, and reusable features across training and inference environments (e.g., using SageMaker Feature Store , Feast, or custom implementation). - Work closely with data scientists … to formalize feature engineering workflows , ensuring traceability, scalability, and maintainability of features. - Develop unit, integration, and data validation tests for models and features to ensure stability and quality. - Establish model monitoring and alerting frameworks for real-time and batch inference (e.g., model drift detection, performance degradation). - Build CI/CD pipelines for ML workflows (training, evaluation, deployment More ❯

skills – even better if these skills have been applied to threat detection, malware analysis, phishing and/or abuse detection. Experience building production-grade AI pipelines, including data ingestion, feature engineering, validation, model deployment, and monitoring. Experience designing and implementing anomaly detection, classification, clustering, and retrieval across vision and language models, ideally for identifying cyber threats (URLs, domains … fit to problem space, including scenarios where RAG is applicable. Incident response experience, and ability to work with large, noisy, and rapidly evolving threat datasets. Strong background in cloud engineering and containerisation (Docker, Kubernetes) with experience deploying AI services at scale, particularly on AWS via Terraform. The package offered to the Senior/Principal AI Engineer will consist of More ❯

skills – even better if these skills have been applied to threat detection, malware analysis, phishing and/or abuse detection. Experience building production-grade AI pipelines, including data ingestion, feature engineering, validation, model deployment, and monitoring. Experience designing and implementing anomaly detection, classification, clustering, and retrieval across vision and language models, ideally for identifying cyber threats (URLs, domains … fit to problem space, including scenarios where RAG is applicable. Incident response experience, and ability to work with large, noisy, and rapidly evolving threat datasets. Strong background in cloud engineering and containerisation (Docker, Kubernetes) with experience deploying AI services at scale, particularly on AWS via Terraform. The package offered to the Senior/Principal AI Engineer will consist of More ❯

skills – even better if these skills have been applied to threat detection, malware analysis, phishing and/or abuse detection. Experience building production-grade AI pipelines, including data ingestion, feature engineering, validation, model deployment, and monitoring. Experience designing and implementing anomaly detection, classification, clustering, and retrieval across vision and language models, ideally for identifying cyber threats (URLs, domains … fit to problem space, including scenarios where RAG is applicable. Incident response experience, and ability to work with large, noisy, and rapidly evolving threat datasets. Strong background in cloud engineering and containerisation (Docker, Kubernetes) with experience deploying AI services at scale, particularly on AWS via Terraform. The package offered to the Senior/Principal AI Engineer will consist of More ❯

What you will do In this role, you will provide technical leadership in the development of our Causal AI platform, guiding both the vision and execution within our Product Engineering team, which includes software engineers, data scientists, and machine learning experts. While you will actively contribute to coding and algorithm development, you will also play a pivotal role in … on: Leading the design and development of advanced Causal AI algorithms, with a focus on time series and tabular data, ensuring they are optimised for scalability and performance. Overseeing feature engineering and machine learning initiatives to deliver robust, production-quality solutions. Providing mentoring and technical guidance to junior engineers and data scientists, fostering a culture of continuous learning … product management, DevOps, and UX/UI design, to seamlessly integrate Causal AI capabilities into our platform's architecture. A minimum of 5 years of experience in machine learning engineering or a related field, with demonstrated success in deploying machine learning models into production environments. Strong academic background in a quantitative discipline (e.g., machine learning, statistics, mathematics) or equivalent More ❯

clustering, and retrieval across vision and language models, ideally for identifying cyber threats (URLs, domains, phishing, botnets, etc.) Hands-on experience building production -grade AI pipelines, including data ingestion, feature engineering, validation, model deployment, and monitoring . Proficient in a major backend language and related ML/AI libraries (e.g. Tensorflow & PyTorch , etc), with a preference for Go. … to problem space , including scenarios where RAG is applicable. Incident response experience, and ability to work with large, noisy, and rapidly evolving threat datasets . Strong background in cloud engineering and containerisation (Docker, Kubernetes ), with experience deploying AI services at scale, particularly on AWS via Terraform . Bonus points if you have: Experience with P erl . Experience leveraging More ❯

packaging and execution. Building out our offering around data modeling. You won't just work on the data models themselves - you'll work closely with Product and the wider Engineering team to shape the way we collect data via our trackers to build better data models, and drive what data model tooling we provide as part of our commercial … batch and streaming data processing . You have experience building streaming pipelines using tools like Benthos , enabling real-time data ingestion, transformation, and delivery across various systems. You understand feature engineering and management. You're familiar with tools like Feast for defining, materializing, and serving features in both real-time and batch contexts. You have extensive experience using … Python which is used for auto generating data models. You are not new to engineering . You use CI/CD, and Git source control as part of your daily job. You have experience with testing frameworks. You are a proactive learner . Eager to expand on your software engineering knowledge and adapt to new technologies essential for More ❯