101 to 125 of 140 Feature Engineering Jobs in London

Build and maintain end-to-end ML pipelines covering data preparation, experimentation, training, evaluation, deployment, monitoring, and optimisation. Develop large-scale data processing and feature engineering solutions using technologies such as PySpark and Databricks. Create multimodal machine learning models that combine image, text, metadata, and behavioural data … productionise innovative machine learning solutions. YOUR KEY SKILLS Significant experience designing, deploying, and supporting machine learning systems in production environments. Strong Python software engineering expertise, including testing, code quality, and maintainable application development. Proven experience working with large-scale data processing technologies such as PySpark and Databricks. Strong background ...

driven and AI/ML use cases. -Build, train, evaluate, and fine-tune machine learning models for structured and unstructured data. -Perform data analysis, feature engineering, and experimentation to validate hypotheses. -Develop efficient SQL queries for data extraction, transformation, and analysis from relational databases. -Integrate prototypes with APIs ...

machine learning models for user personalisation, including recommendation engines, ranking algorithms, user segmentation, and content analysis Build and maintain robust, scalable data pipelines for feature engineering and model training using structured and unstructured large-scale datasets Deploy and supervise ML models in production environments to ensure high availability ...

related field. Python skills, with experience applying statistical analysis and time series techniques to complex datasets. Strong understanding of data quality assessment, feature engineering, and modelling. Strong communication skills with the ability to explain technical findings to both technical and non-technical stakeholders. Experience evaluating new datasets ...

extraction/resolution Knowledge Graphs; building and inference, graphRAG Recommendation systems and ranking: personalisation, learning-to-rank, feed ranking, user modelling, etc. Tabular ML: feature engineering, structured data modelling (GBDTs, etc.) Statistical & Predictive Modelling: supervised ML, anomaly detection, temporal modelling, etc. Deep understanding of the GenAI landscape ...

scale retrieval. Mentor, coach, and set technical direction within the Recommendations team, helping others grow and innovate. Collaborate closely with cross-functional partners (product, engineering, data) to define problems, translate them into scalable solutions, and deliver measurable business outcomes. Lead the end-to-end lifecycle of ML projects: from … ideation, data acquisition, feature engineering, training, and evaluation to deployment and ongoing iteration. Drive innovation in recommendation systems by researching and integrating emerging ML techniques, frameworks, and tooling, while contributing technical expertise to long-term product and data strategy. Act as a thought leader in the recommendations space ...

advanced ranking models for Depop’s entire app, covering personalised recommendations, search results and other surfaces. Collaborate closely with cross-functional partners (product, engineering, data) to define problems, translate them into scalable solutions, and deliver measurable business outcomes. Lead the end-to-end lifecycle of ML projects: from ideation … data acquisition, feature engineering, training, and evaluation to deployment and ongoing iteration. Drive innovation in ranking by researching and integrating emerging ML techniques, frameworks, and tooling, while contributing technical expertise to long-term product and data strategy. Mentor, coach, and set technical direction within the Ranking ML team ...

Skills & Experience Required – Technical Either MSc in Statistics or a related field (e.g., Data Science or Mathematics), or another field (such as Computer Science, Engineering, Finance, etc) with some experience in statistics and 3+ years of work experience in a relevant role (e.g. sports quantitative analyst for a betting … exchange); or a PhD or equivalent in Statistics or a related field (e.g., Data Science or Mathematics), or another field (such as Computer Science, Engineering, Finance, etc) with some experience in statistics Extensive experience of probabilistic and statistical modelling Model diagnostics: Ability to evaluate predictive model performance using appropriate ...

software and technical infrastructure. The role involves the development and maintenance of sophisticated tools for alpha research along with the production systems used in feature engineering, portfolio construction, and trade execution. The ideal candidate should be an expert engineer with a deep theoretical foundation, extensive systems design experience … Pytorch, etc. DevOps: version control, testing frameworks, release processes, build systems. Excellent communication, problem-solving, and analytical skills. Preferred ExperienceExtremely strong computer science or engineering background with 5+ years of experience. Experience designing and implementing: Distributed Systems. Real-time event-driven systems. Large-scale time series data ingress, storage ...

This role is responsible for the delivery of data-driven applications and analytical solutions across the programme, combining expertise in Oracle APEX development, analytics engineering and data science enablement. The position will design and develop reporting solutions, dashboards, lightweight applications and analytical proofs of concept (PoCs), leveraging the client … data visualisations, semantic models and automated reporting solutions, translating complex data into meaningful business insights. Support data science and AI initiatives through data preparation, feature engineering and model input development, collaborating with AI Engineers to deliver analytical proofs of concept (PoCs). Work across Oracle Autonomous Data Warehouse ...

Data Scientist to join our Data Practice, delivering enterprise-scale data platforms, analytics and AI-enabled decision support. You will contribute across model development, feature engineering, training and deployment, collaborating with engineers and consultants. Ideal candidates will have 3–5 years in data science, strong Python ...

different backgrounds Create and maintain post-trade diagnostics tools across various strategies Qualifications: A bachelor’s, master’s, or PhD in mathematics, statistics, physics, engineering, computer science or related field Experience using AI and LLM’s Passion and interest for a quant researcher role solving real-world business problems … Minimum of two years' experience in alpha generating role Experience working with large datasets, data cleaning, preprocessing, and feature engineering Experience building, validating, deploying, monitoring, and updating predictive models Excellent programming and scripting skills Experience working independently and delivering results Excellent written and oral communication skills, experience communicating ...

Fincrime - Scam Preventionat Wise • Worship St, London, UKBack to jobs1. Home2. Jobs3. UK4. Software Engineer - Fincrime - Scam Prevention—W## Software Engineer - Fincrime - Scam PreventionWise20Software Engineering, Fincrime, Scam PreventionFull-timeGBP 68,000 - GBP 87,500 - yearlyWorship St, London, UKApply Job### Job DescriptionWise is a global technology company, building the best … world's money. For everyone, everywhere. Join us as a Software Engineer in our critical Scam Prevention team. This is a high-impact engineering role with a strong product focus, dedicated to solving complex, real-world adversarial problems to directly protect our global customer base. The Scam Prevention team ...

Development: Design, train, and optimise machine learning models focused on user personalisation, encompassing recommendation engines, ranking algorithms, user segmentation, and content analysis. Data Pipeline Engineering: Construct and maintain robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets. ...

Development: Design, train, and optimise machine learning models focused on user personalisation, encompassing recommendation engines, ranking algorithms, user segmentation, and content analysis. Data Pipeline Engineering: Construct and maintain robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets. ...

optimise machine learning models focused on personalisation, recommendation engines, ranking algorithms, user segmentation and content analysis. Develop and maintain scalable data pipelines to support feature engineering, model training and deployment across large structured and unstructured datasets. Deploy, monitor and maintain machine learning models in production environments, ensuring performance ...

Senior Decision Scientist Build and deploy ML and statistical models for credit risk, affordability, and pricing Own the full model lifecycle, including data, feature engineering, modelling, validation, deployment and monitoring Develop monitoring frameworks and performance dashboards Drive innovation through new data sources, challenger models and experimentation (A/ ...

seeking a highly motivated Quantitative Software Engineer/Quantitative Developer to join their Systematic Strategies team in London. This is a hands‐on engineering role focused on building and scaling the technology platform that underpins the firm's systematic investment strategies. Working directly alongside Quantitative Researchers, Portfolio Managers … developing the infrastructure, tooling and production systems that support the full life-cycle of systematic trading strategies. The ideal candidate will have a strong engineering mindset, a deep understanding of quantitative research workflows, and experience building robust, scalable systems used in systematic trading environments. This role offers the opportunity ...

procurement. We are seeking an experienced data scientist to develop forecast models for energy, metals, and other inputs, owning the full modelling lifecycle from feature engineering to deployment. You will integrate diverse data sources and translate probabilistic forecasts into procurement decisions, collaborating closely with our engineering team ...

side with experienced researchers who arecommitted to teaching, guiding, and supporting ournewest hires, learning how we think aboutexperiment design, dataset generation, time seriesanalysis, feature engineering, and model buildingfor financial datasets. At Jane Street, our researchers, engineers, andtraders sit a few feet away from each other andwork together … like to learn more, you can read aboutour interview process and meet some of the team onour website. Computer Science, Data, AI,Engineering & Technology,Life and Health Sciences, Medicine,Natural Sciences & Mathematics Enterpreneurs wanted! TUM support for prospective startups. Practicalities regarding jobmarket regulations for international students. #J-18808-Ljbffr ...

Category: Non-life Actuarial Type: Permanent Key Responsibilities (including but not limited to): Own the end-to-end modelling lifecycle: problem framing, data build, feature engineering, model development, validation, documentation Build and maintain risk and price models using GLMs and machine learning Translate models into implementable rating structures ...

working closely with Reserving and Claims Analytics teams. Own documentation and governance for model approval ahead of deployment. Test new data sources, factors and feature engineering to continually improve model predictiveness. Present model performance against agreed KPIs to key stakeholders. Mentor junior analysts, embedding best practice across ...

scoring and portfolio valuation models. You will design and maintain real-time PD models, calibrate frameworks, and ensure regulatory fairness while exploring LLM-based feature engineering. You will collaborate with cross-functional teams, mentor juniors, and contribute to long-term modeling vision, leveraging Python, SQL, XGBoost, and MLFlow ...

build the core intelligence engine for its vulnerability correlation platform in London. This hands-on role focuses on deterministic data pipelines, entity resolution, feature engineering, and scalable architectures. You will balance pragmatic engineering with strategic product sense, deciding what to build in-house versus reuse, integrating semantic ...

domains involving high-frequency or large-scale time-series data. You will focus on developing alpha signal generation pipelines from data ingestion and feature engineering to model training and deployment - in collaboration with our trading and infrastructure teams. Responsibilities: Develop ML-based alpha generation models using high-frequency … order book and market microstructure data. Design and maintain data pipelines, preprocessing, and feature extraction workflows tailored to streaming tick data. Research and implement advanced deep learning architectures for short-horizon forecasting and signal extraction. Collaborate with quant researchers and developers to integrate models into live trading environments. Optimise ...