16 of 16 Feature Engineering Jobs in the City of London

multiple internal and external sources. Perform exploratory data analysis (EDA) to identify trends, patterns, and anomalies. Design and implement data pipelines for model-ready datasets in collaboration with data engineering teams. Apply feature engineering and selection techniques to improve model accuracy and interpretability. Develop and validate machine learning and statistical models for prediction, classification, clustering, or optimization. … on project requirements. Evaluate models using appropriate metrics and perform hyperparameter tuning for optimal performance. Convert proof-of-concept models into production-grade pipelines in collaboration with MLOps and engineering teams. Required: Translate model outcomes into actionable insights through clear storytelling and visualizations. Build dashboards and reports using Power BI, Tableau, or Python-based visualization tools. Communicate findings to More ❯

Responsibilities - Lead end-to-end execution of complex data science projects integrating statistical modelling, machine learning (ML), and deep learning (DL). - Collaborate closely with cross-functional teams (product, engineering, business stakeholders) to deliver data-driven solutions. - Design and conduct hypothesis testing (A/B and multivariate testing) and apply causal inference methodologies. - Perform exploratory data analysis, feature engineering, and develop models across traditional statistical and deep learning architectures. - Establish modelling frameworks with statistical quality control, detecting drift and decay. - Drive best practices for model explainability (e.g., SHAP/LIME) and scalable ML systems (including generative AI, NLP, CV, and recommendation engines). - Partner with engineering teams to ensure robust deployment and adherence to MLOps … learning fundamentals, statistical inference, and model evaluation. - Advanced proficiency in SQL (e.g., PostgreSQL, ELT/ETL) and Python (PyTorch, LightGBM, Scikit-learn). - Experience with modern AI concepts: prompt engineering, embeddings, vector search, etc. - Skilled in managing complex codebases (Git) and working with cloud platforms (GCP, AWS). - Excellent analytical, communication, and organisational skills. Desirable - MS/PhD in More ❯

machine learning and AI models Designing, developing, and deploying LLM applications (e.g. GPT, LLaMA, Claude) integrated with RAG pipelines Implementing end-to-end workflows: from data acquisition, cleaning, and feature engineering to model training, deployment, and monitoring Building scalable pipelines and APIs for AI services in cloud environments (AWS, Azure, or GCP) Collaborating with engineers, product teams, and … advancements in AI and machine learning What We’re Looking For: A recently completed Master’s degree from a Russell Group university in Artificial Intelligence, Computer Science, Data Science, Engineering, Mathematics, or a related discipline Demonstrated project experience (academic research, dissertation work, or personal projects) applying machine learning or AI techniques Strong programming skills in Python (e.g. PyTorch, TensorFlow … remote work 📈 Career Growth: Mentorship, structured training, and clear progression paths 🛠 Modern Tech Stack: Hands-on with the latest AI and cloud tools 🤝 Collaborative Culture: Join a supportive, innovative engineering team ✨ Additional Perks: Pension scheme, private healthcare, and wellbeing initiatives How to Apply: If you’re excited by the opportunity to apply your academic and project work to real More ❯

Responsibilities Model Development & Deployment: Design, build, and deploy scalable machine learning and statistical models for use cases such as pricing optimisation, customer behaviour prediction, and risk modelling. Data Exploration & Feature Engineering: Work with structured and unstructured data to uncover patterns and develop robust features for predictive models. Stakeholder Engagement: Collaborate with Technology, Product, Risk, and Marketing teams to More ❯

powered tools (e.g. using GPT-class models) that assist with data extraction, document understanding, investor reporting, and internal decision-support Implementing end-to-end AI pipelines: data ingestion, cleaning, feature engineering, experimentation, model training, evaluation, and deployment Developing robust evaluation and monitoring frameworks for models (including regression tests, performance tracking, and drift detection) Working with structured and unstructured … founders, fast progression in a high-growth AI-driven fintech 🧠 Deep Technical Work: Opportunity to work on challenging, high-impact AI problems with real financial data and users 🛠 Modern Engineering Practices: Exposure to modern MLOps, experimentation workflows, and best practices in production AI 🤝 Collaborative Culture: Join a supportive, intellectually rigorous team that values deep thinking, ownership, and high-quality … engineering ✨ Additional Perks: Pension scheme, private healthcare, regular team socials, and wellbeing initiatives How to Apply: If you’re excited by the opportunity to apply your AI Master’s training to real-world problems in financial technology, please send us your CV. A member of the team will be in touch to discuss next steps. More ❯

workflows on AWS Building LLM applications (e.g. GPT, LLaMA, Claude) that automate policy checks, documentation, and audit trails Implementing end-to-end AI workflows: from data acquisition, cleaning, and feature engineering to model training, deployment, and monitoring Developing scalable pipelines and APIs for AI services using AWS-native infrastructure (e.g. Lambda, ECS/EKS, S3, API Gateway, Step … Looking For: A recently completed Master’s degree from a top-tier university (e.g. Oxbridge, Imperial, UCL, or other leading Russell Group) in Artificial Intelligence, Computer Science, Data Science, Engineering, Mathematics, or a related discipline Demonstrated project experience (academic research, dissertation work, internships, or personal projects) applying machine learning or AI techniques Strong programming skills in Python and TypeScript More ❯

the following areas is a plus: finance, open banking, iGaming, startups, or enterprise companies dealing with real-time processing. Responsibilities You will use Python, with a strong grounding in feature engineering, model evaluation, and inference pipelines to help shape the future of our product offerings Lead data labeling at scale to produce ground-truth datasets and use ML … deployment and data workflows. Strong knowledge of statistical modelling, anomaly detection, clustering, and supervised/unsupervised learning Experience working with large-scale data Proven success collaborating with product and engineering teams to ship ML-based features and tools Strong communication skills and business acumen to present complex technical ideas to non-technical stakeholders Curious, proactive, and comfortable working in More ❯

Framework Design: Develop and apply a structured maturity model to assess how data science work is conceived, executed, validated, and scaled. • Model Lifecycle Review: Assess practices across data preparation, feature engineering, model development, validation, monitoring, and iteration. • Tooling & Workflow Analysis: Review the ecosystem of analytical tools, frameworks, and environments used for data science, including reproducibility and collaboration readiness. 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 … and optimisation techniques Comfortable working with cross-functional teams, including engineers, product leads, and client stakeholders Bachelor’s or Master’s degree in a quantitative field (e.g., Mathematics, Physics, Engineering, Computer Science) from a strong university Excellent communication skills and a collaborative mindset Please note: This role cannot offer VISA sponsorship 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 Act as a technical lead within project teams, mentoring mid … and optimisation techniques Comfortable working with cross-functional teams, including engineers, product leads, and client stakeholders Bachelor’s or Master’s degree in a quantitative field (e.g., Mathematics, Physics, Engineering, Computer Science) from a strong university Excellent communication skills and a collaborative mindset Please note: This role cannot offer VISA sponsorship More ❯

impact. You’ll remain hands-on in design and experimentation while mentoring 1–2 junior ML engineers. You’ll collaborate closely with credit analysts, data scientists, and the wider engineering team to ensure our models are accurate, explainable, and seamlessly integrated into our lending platform. What You’ll Do Lead the design, training, and optimisation of credit risk and … XGBoost and scikit-learn. Responsible for creating proprietary data enrichment algorithms. Guide the evolution toward a self-learning model framework, improving automation and adaptability over time. Design and oversee feature testing and evaluation to enhance predictive performance and interpretability. Use BigQuery and GCP tools (including CloudRun) to manage and process large-scale datasets efficiently. Ensure models are explainable and … teams to interpret outcomes. Mentor junior ML engineers through code reviews, technical guidance, and project planning. Work with software engineers to productionise models (deployment and pipelines handled by the engineering team). Stay ahead of emerging ML techniques and bring new ideas to improve scalability, performance, and transparency. What We’re Looking For 4+ years of hands-on experience More ❯

as; AI Foundry, Open-AI services, and Microsoft Copilot Studio. Apply governance frameworks for AI/ML models. Python knowledge to automate processes Large datasets experience, including data preprocessing, feature engineering, and model evaluation. Agile development of AI solution from concept to deployment to continuous improvement. Create and maintain technical documentation Communicate complex concepts to all stakeholders. At … least 3 years of experience in AI solution engineering. Large Language Models experience including prompt engineering and RAG implementations. Expert data analytics, MLOps practices and API development. Desirable knowledge in Docker and Kubernetes More ❯

is placing data science at the heart of its strategy, investing heavily in both classic ML and next-generation AI capabilities. This is a company that blends modern product engineering with a fast-moving, high-ownership culture. Their environment encourages experimentation, cross-functional collaboration, and the freedom to shape what “great” looks like in machine learning at scale. The … of their real-time modelling infrastructure. This is a hybrid hands-on/leadership role, ideal for someone who thrives at the intersection of applied research, platform integration, and engineering-minded data science. What You’ll Be Doing Build and deploy a wide variety of models spanning classification, regression, propensity scoring, and LLM-based use cases Spearhead the entire … s GenAI efforts. The team have multiple LLM projects running but would love a technical leader in this area Own the end-to-end lifecycle of ML projects, from feature engineering to deployment and monitoring Define best practices for model testing, automation, and continuous improvement within a high-performing team Act as a technical thought leader, partnering with More ❯

using dbt and Delta Live Tables to ensure consistency across analytics and AI use cases Enable Generative AI and ML workloads by designing pipelines for vector search, RAG, and feature engineering Implement secure access and governance controls including RBAC, SSO, token policies, and pseudonymisation frameworks Support batch and streaming data flows using technologies like Kafka, Airflow, and Terraform More ❯

a forward-thinking trading organisation. This is an exciting opportunity to design and implement the infrastructure that powers advanced machine learning workflows in a production trading environment. Key Responsibilities: Feature Store & Data Lake: Build scalable infrastructure for time-series feature storage, retrieval, and versioning optimised for ML workloads. MLOps Pipelines: Design end-to-end workflows for data ingestion … feature engineering, model training, backtesting, and deployment. Data Ingestion Layer: Connect raw data streams into structured, queryable formats (Parquet/Delta Lake). Production Serving: Deploy feature computation and model inference with appropriate latency characteristics. Integration: Collaborate with existing data capture and execution systems to ensure seamless operations. CI/CD Pipeline: Implement and maintain robust continuous … integration and deployment pipelines for ML models. Requirements: Strong experience in building and maintaining MLOps pipelines. Hands-on experience with feature stores, data lakes, and time-series data. Proficiency with modern data formats like Parquet and Delta Lake. Familiarity with production ML model deployment and latency optimisation. Experience integrating ML workflows with existing data and execution systems. Strong understanding More ❯

Work with world-class researchers and portfolio managers where every model you build has real market impact . Take ownership of the full lifecycle: from messy, high-frequency datasets → feature engineering, training and deployment, live inference. Shape the firm’s ML ecosystem, choosing the right tools and frameworks to deliver performance at scale. Tackle challenges you won’t … low-latency requirements, and models that need to work in the wild, not just in notebooks . What We’re Looking For Strong hands-on experience with machine learning engineering at scale . Expertise in Python (plus C Java a bonus), and familiarity with ML frameworks like PyTorch, TensorFlow, Scikit-learn . Experience building production-grade pipelines (Spark, Ray More ❯