18 of 18 Feature Engineering Jobs in Central 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 ❯

clean, and preprocess structured and unstructured data from diverse sources Perform exploratory data analysis (EDA) to uncover trends and anomalies Design and implement data pipelines in collaboration with data engineering teams Apply feature engineering and selection techniques to enhance model performance Build and validate ML models for prediction, classification, clustering, and optimization Use libraries such as Scikit 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 ❯

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 ❯

ML team to build and deploy GenAI solutions to production, using existing and emerging methods and technologies that could be effectively applied to use cases Build core infrastructure around feature engineering, model training, model deployment, ongoing monitoring tools, and much more. Building and define key evaluation systems and processes to ensure GenAI Tooling performs at a high standard … Help guide and define strategic ambitions relevant to GenAI tooling and projects Work closely with Infrastructure, data engineering and DevOps teams to increase deployment velocity, including the process for deploying tools into production Work closely with Product Management to translate product requirements into robust, customer-agnostic machine learning architectures Keep abreast of new engineering practices, technologies and continuously … physical trading across energy commodities (oil, gas, power, renewables, and related derivatives) Risk - Modelling & understanding of risk & risk management Commodity Modelling - Energy (power, gas, environmental products) and meteorology Data engineering skills using Azure/AWS/Google cloud native tools Good experience in Bash\Powershell Scripting and Linux Operating system and networking fundamentals Front end application development Active in More ❯

ML team to build and deploy GenAI solutions to production, using existing and emerging methods and technologies that could be effectively applied to use cases Build core infrastructure around feature engineering, model training, model deployment, ongoing monitoring tools, and much more. Building and define key evaluation systems and processes to ensure GenAI Tooling performs at a high standard … Help guide and define strategic ambitions relevant to GenAI tooling and projects Work closely with Infrastructure, data engineering and DevOps teams to increase deployment velocity, including the process for deploying tools into production Work closely with Product Management to translate product requirements into robust, customer-agnostic machine learning architectures Keep abreast of new engineering practices, technologies and continuously … physical trading across energy commodities (oil, gas, power, renewables, and related derivatives) Risk - Modelling & understanding of risk & risk management Commodity Modelling - Energy (power, gas, environmental products) and meteorology Data engineering skills using Azure/AWS/Google cloud native tools Good experience in Bash\Powershell Scripting and Linux Operating system and networking fundamentals Front end application development Active in 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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯