1 to 25 of 53 Feature Engineering Jobs in London

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 deliver NLP based machine learning systems at scale that drive measurable impact for our business Own the full end to end machine learning delivery lifecycle including data exploration, feature engineering, model selection and tuning, offline and online evaluation, deployments and maintenance Partner with stakeholders to propose innovative data products that leverage Trainline's extensive datasets and state … and platforms like ML Flow Have experience with agile delivery methodologies and CI/CD processes and tools Have a broad of understanding of data extraction, data manipulation and feature engineering techniques Are familiar with statistical methodologies. Have good communication skills Nice to have Experience with LangGraph or LangChain Experience with transport industry and/or geographical information More ❯

Responsibilities Lead the design, development, and maintenance of credit risk and affordability models using bureau, open banking, and behavioural data Own the full model lifecycle from data sourcing and feature engineering to validation, deployment, and monitoring Design and run A/B and champion/challenger tests to improve performance across approval rates, losses, and customer experience Analyse … credit performance data to deliver insights that guide strategic decisions Mentor and support junior analysts/data scientists as the team expands Collaborate with data engineering to deploy models into production Work closely with stakeholders to define goals, communicate findings, and translate model outputs into business value About You You are an analytical thinker with a passion for using … data to drive credit decisioning. You bring hands-on experience building machine learning models for consumer credit and understand the nuances of data preparation, feature selection, and model validation in high-stakes environments. 5–7 years of experience in a credit-related data science or decision science role Proficiency in Python and experience with libraries such as scikit-learn More ❯

Responsibilities Lead the design, development, and maintenance of credit risk and affordability models using bureau, open banking, and behavioural data Own the full model lifecycle from data sourcing and feature engineering to validation, deployment, and monitoring Design and run A/B and champion/challenger tests to improve performance across approval rates, losses, and customer experience Analyse … credit performance data to deliver insights that guide strategic decisions Mentor and support junior analysts/data scientists as the team expands Collaborate with data engineering to deploy models into production Work closely with stakeholders to define goals, communicate findings, and translate model outputs into business value About You You are an analytical thinker with a passion for using … data to drive credit decisioning. You bring hands-on experience building machine learning models for consumer credit and understand the nuances of data preparation, feature selection, and model validation in high-stakes environments. 5–7 years of experience in a credit-related data science or decision science role Proficiency in Python and experience with libraries such as scikit-learn More ❯

Responsibilities Lead the design, development, and maintenance of credit risk and affordability models using bureau, open banking, and behavioural data Own the full model lifecycle from data sourcing and feature engineering to validation, deployment, and monitoring Design and run A/B and champion/challenger tests to improve performance across approval rates, losses, and customer experience Analyse … credit performance data to deliver insights that guide strategic decisions Mentor and support junior analysts/data scientists as the team expands Collaborate with data engineering to deploy models into production Work closely with stakeholders to define goals, communicate findings, and translate model outputs into business value About You You are an analytical thinker with a passion for using … data to drive credit decisioning. You bring hands-on experience building machine learning models for consumer credit and understand the nuances of data preparation, feature selection, and model validation in high-stakes environments. 5–7 years of experience in a credit-related data science or decision science role Proficiency in Python and experience with libraries such as scikit-learn More ❯

Responsibilities Lead the design, development, and maintenance of credit risk and affordability models using bureau, open banking, and behavioural data Own the full model lifecycle from data sourcing and feature engineering to validation, deployment, and monitoring Design and run A/B and champion/challenger tests to improve performance across approval rates, losses, and customer experience Analyse … credit performance data to deliver insights that guide strategic decisions Mentor and support junior analysts/data scientists as the team expands Collaborate with data engineering to deploy models into production Work closely with stakeholders to define goals, communicate findings, and translate model outputs into business value About You You are an analytical thinker with a passion for using … data to drive credit decisioning. You bring hands-on experience building machine learning models for consumer credit and understand the nuances of data preparation, feature selection, and model validation in high-stakes environments. 5–7 years of experience in a credit-related data science or decision science role Proficiency in Python and experience with libraries such as scikit-learn More ❯

Aimpoint Digital is a premier analytics consulting firm with a mission to drive business value for clients through expertise in data strategy, data analytics, decision sciences, and data engineering and infrastructure. This position is within our decision sciences practice which focuses on delivering solutions via machine learning and statistical modelling. What you will do As a part of Aimpoint … Become a trusted advisor working with clients to design end-to-end analytical solutions Work independently to solve complex data science use-cases across various industries Design and develop feature engineering pipelines, build ML & AI infrastructure, deploy models, and orchestrate advanced analytical insights Write code in SQL, Python, and Spark following software engineering best practices Collaborate with … impact for your clients and do so through thoughtfulness, prioritization, and seeing a solution through from brainstorming to deployment. In particular you have these traits: Degree in Computer Science, Engineering, Mathematics, or equivalent experience. Experience with building high quality Data Science models to solve a client's business problems Experience with managing stakeholders and collaborating with customers Strong written More ❯

and visibility functionality. As a senior member of the team, you'll be self motivated and be able to take ownership of projects, collaborate closely with stakeholders across product, engineering, and business to deliver data science solutions. Responsibilities Work across and support a range of data use cases including analytics and data provisioning Lead the design, development, and deployment … models and advanced analytics solutions to support recommendation and insight to production use cases Apply statistical techniques to extract insights and support data-driven decision-making Work alongside data engineering in requirements for data pipelines and feature engineering Promote best practices in data science, model validation, documentation, and reproducibility Qualifications Experience Strong coding skills in Python, including More ❯

ll Bring: We are seeking a seasoned professional who is excited by the unique challenges of AI data. Qualifications What are we looking for? Must-Have Skills: Extensive Data Engineering Experience: Proven track record (3+ years) in designing, building, and maintaining large-scale data pipelines and data warehousing solutions. Cloud Platform Mastery: Expert-level proficiency with at least one … data technologies like Apache Spark, Kafka, and data orchestration tools such as Apache Airflow or Prefect. ML Data Acumen: Solid understanding of data requirements for machine learning models, including feature engineering, data validation, and dataset versioning. Vector Database Experience: Practical experience working with vector databases (e.g., Pinecone, Milvus, Chroma) for embedding storage and retrieval. Generative AI Familiarity: Understanding … TensorFlow for data preparation in an ML context. Experience with real-time data streaming architectures. Familiarity with containerization (Docker, Kubernetes). Master's or Ph.D. in Computer Science, Data Engineering, or a related quantitative field. Additional Information Starcom has fantastic benefits on offer to all of our employees. In addition to the classics,Pension,Life Assurance, Private Medical and More ❯

from large-scale sports datasets using sound mathematical and statistical principles. Translate modelling requirements and business objectives into effective data science solutions, working closely with the Delivery Manager and Engineering teammates (Software and Data Engineers) within your modelling team. Perform data cleaning, exploratory data analysis (EDA), feature engineering, and model evaluation to support continuous model improvement. Write More ❯

from large-scale sports datasets using sound mathematical and statistical principles. Translate modelling requirements and business objectives into effective data science solutions, working closely with the Delivery Manager and Engineering teammates (Software and Data Engineers) within your modelling team. Perform data cleaning, exploratory data analysis (EDA), feature engineering, and model evaluation to support continuous model improvement. Write More ❯

comfortable supporting (or willing to learn) in the following areas: Programming for Data Science (e.g., Python with Pandas, NumPy, R) Statistical Analysis and Hypothesis Testing Data Cleaning, Preprocessing, and Feature Engineering Data Visualization (e.g., Matplotlib, Seaborn, Plotly, Tableau) Machine Learning Fundamentals (e.g., Supervised, Unsupervised Learning) Machine Learning Algorithms (e.g., Regression, Classification, Clustering, Decision Trees, SVMs, Neural Networks) Model More ❯

/CD pipelines and automated deployment practices. Familiarity with DevOps concepts, including containerization (e.g., Docker), orchestration (e.g., Kubernetes), and cloud infrastructure management (AWS, Azure, GCP). Experience in data engineering within Big Data ecosystems, including data pipelines and data integration. Solid understanding of software architecture and system design for high-availability applications. Knowledge of fundamental computer science concepts, including … maintain high-quality software development processes. Stay updated on the latest technologies and apply innovative solutions where applicable. Nice-to-Have Responsibilities (ML Focus): Develop, refine, and utilize ML engineering platforms and components as needed. Establish and manage processes for data preparation, feature engineering, and prediction. Closely monitor model performance and address any issues that arise. Explore More ❯

platforms (GCP preferred, AWS and Azure acceptable) Familiarity with Python ML packages such as PyTorch, TensorFlow, XGBoost, scikit-learn, SciPy, NumPy, pandas Strong SQL skills for data preparation and feature engineering Knowledge of MLOps principles, including automated retraining, monitoring, and deployment strategies Basic understanding of containerization and tools like Docker Nice-to-have skills: Experience with Google Cloud More ❯

Experience with common Python packages for Machine Learning - examples include PyTorch, TensorFlow, XGBoost, scikit-learn, SciPy, NumPy, pandas, etc. * Strong knowledge of SQL and its use for data preparation & feature engineering * Understanding of & practical experience with implementing MLOps principals - including automated model retraining, monitoring & deployment strategies * Some knowledge of containerisation & use of tools like Docker & Docker Compose Nice More ❯

machine learning and deep learning models. Contribute to scalable and reusable data pipelines using modern ML workflows. Conduct experiments and benchmarking exercises to test model performance. Perform error analysis, feature importance, and other model diagnostics. Track and log training/testing outcomes to support reproducibility and model versioning. Engineering Contributions Help build and integrate AI-powered APIs, scripts … tools, and containerization (e.g., Docker) to maintain codebase quality. Applied AI Domains Work on projects involving Natural Language Processing (NLP), Computer Vision, Generative AI, or Recommendation Systems. Support annotation, feature engineering, and augmentation tasks where necessary. Write clear, well-organized documentation for code, models, datasets, and workflows. Participate in team meetings, sprint planning, and code reviews. Engage with … to reflect on progress, set learning goals, and track outcomes. Required Qualifications A Bachelor's or Master's degree (completed or ongoing) in Computer Science, Data Science, Mathematics, Software Engineering, or a related STEM field. Eligibility to enroll in a Level 6 or Level 7 AI/ML/Data Science apprenticeship programme. Core Skills & Competencies Technical Skills Programming More ❯

relevance. Build production-ready Python code and integrate models into user-facing applications. Monitor and refine model performance post-deployment, making improvements based on feedback. Collaborate with product and engineering teams to deliver solutions aligned with business goals. Role Requirements 2-4 years' experience in applied machine learning and generative AI, including work with large language models. Strong Python … core ML libraries (numpy, pandas, scikit-learn, boosting methods). Proven ability to design, test, and deploy production-ready machine learning solutions. Hands-on experience in data processing and feature engineering for complex datasets. Solid understanding of machine learning algorithms and statistical modelling techniques. A degree in Computer Science, Statistics, Machine Learning, Engineering, Physics, or a related More ❯

relevance. Build production-ready Python code and integrate models into user-facing applications. Monitor and refine model performance post-deployment, making improvements based on feedback. Collaborate with product and engineering teams to deliver solutions aligned with business goals. Role Requirements 2-4 years' experience in applied machine learning and generative AI, including work with large language models. Strong Python … core ML libraries (numpy, pandas, scikit-learn, boosting methods). Proven ability to design, test, and deploy production-ready machine learning solutions. Hands-on experience in data processing and feature engineering for complex datasets. Solid understanding of machine learning algorithms and statistical modelling techniques. A degree in Computer Science, Statistics, Machine Learning, Engineering, Physics, or a related More ❯

with different machine learning techniques and algorithms, including supervised, unsupervised, semi-supervised, reinforcement, and deep learning; Design and optimize machine learning pipelines and workflows, incorporating techniques for data cleaning, feature engineering, model selection, and hyperparameter tuning; and Develop scalable and efficient machine learning infrastructure and systems for training, testing, and deploying models in production environments. Qualifications Bachelor's More ❯

the organization. Essential Duties Understand business problems and conduct statistical analysis independently. Break down hard problems. Communicate effectively to senior stakeholders. Deliver value end-to-end. Self-serve data engineering and infrastructure as required. Make recommendations on best practice in terms of analysis, machine learning and data science. Have a transformative presence in the team. Develop and implement machine … learning models, including feature engineering, model design, training, and deployment. Perform data mining, exploration, and statistical analysis to uncover trends and actionable insights. Create data visualizations, reports, dashboards, and perform data audits. Leverage predictive models to optimize customer experiences and drive business outcomes. Create automated anomaly detection systems to monitor and ensure data quality and operational performance. Desired … Computer Science, or a related field. D. in a quantitative field such as Statistics, Computer Science, Mathematics, or Engineering. A "full stack" data scientist - with extensive expertise of data engineering, analysis and analytics, as well as machine learning. 6+ years of experience working in Data Science, preferably within a Software organization. Experience with financial fraud detection and prevention is More ❯

deploy predictive models using behavioural and real-world data Translate complex datasets into actionable insights that improve healthcare outcomes Contribute to the full ML lifecycle, from data wrangling and feature engineering to model deployment and monitoring Collaborate with engineering and product teams to ensure models are integrated into production systems via scalable pipelines Champion best practices in More ❯

deploy predictive models using behavioural and real-world data Translate complex datasets into actionable insights that improve healthcare outcomes Contribute to the full ML lifecycle, from data wrangling and feature engineering to model deployment and monitoring Collaborate with engineering and product teams to ensure models are integrated into production systems via scalable pipelines Champion best practices in More ❯

science solutions that deliver commercial value Lead the design, development, and deployment of predictive and optimisation models across multiple industries Own the end-to-end ML pipeline: data exploration, feature engineering, modelling, evaluation, and deployment Collaborate with data engineers and MLOps professionals to ensure scalable, production-grade solutions Conduct rigorous A/B testing and performance measurement to … engineers, product leads, and client stakeholders Experience with A/B testing and causal inference methodologies Bachelor’s or Master’s degree in a quantitative field (e.g., Mathematics, Physics, Engineering, Computer Science) Excellent communication skills and a collaborative mindset Example Projects You Might Work On Optimisation models to increase manufacturing throughput Predictive models for asset maintenance and downtime reduction More ❯

science solutions that deliver commercial value Lead the design, development, and deployment of predictive and optimisation models across multiple industries Own the end-to-end ML pipeline: data exploration, feature engineering, modelling, evaluation, and deployment Collaborate with data engineers and MLOps professionals to ensure scalable, production-grade solutions Conduct rigorous A/B testing and performance measurement to … engineers, product leads, and client stakeholders Experience with A/B testing and causal inference methodologies Bachelor’s or Master’s degree in a quantitative field (e.g., Mathematics, Physics, Engineering, Computer Science) Excellent communication skills and a collaborative mindset Example Projects You Might Work On Optimisation models to increase manufacturing throughput Predictive models for asset maintenance and downtime reduction More ❯

to learn, teach, and ultimately, produce high quality results. Execute data analysis and exploratory analysis (project design, processing of and cleaning of data, merging/joining disparate data sources, feature engineering, performing analyses and communicating results). Produce models using a variety of algorithms (GLM, GBM, Random Forest, Neural Networks etc.) and assess the relative strength of each More ❯