26 to 47 of 47 Feature Engineering Jobs in London

queries and manage remediation actions and findings Your skills and experience Advanced degree (Master’s or PhD) in a quantitative discipline (e.g., Mathematics, Physics, Engineering, Computer Science) 7+ years’ experience developing analytical or machine learning models in financial services (ideally Anti-Financial Crime/Screening) Experience building Natural Language … Processing (NLP)/Machine Learning (ML)/Artificial Intelligence (AI) pipelines, including feature engineering, model training, evaluation and monitoring Experience designing evaluation frameworks for LLM use cases (e.g., human-in-the-loop, calibration, guardrails, auditability) Experience with hyperparameter tuning at scale and experimentation frameworks Strong technical experience with ...

logistic regression, gradient boosting, random forests, clustering, NLP Proficient in Python and/or R, with strong experience in data quality, model development and feature engineering Strong command of SQL and familiarity with data engineering environments such as Databricks or similar Skilled in data visualisation and storytelling ...

audit requirements. Experience with GDPR and AI governance frameworks. Key responsibilities Build and deploy predictive and credit risk models. Lead exploratory data analysis, feature engineering, model validation and monitoring. Develop forecasting, simulation and stress-testing models. Work with data engineering teams to create modelling-ready datasets using ...

Responsibilitie sPerform exploratory data analysis (EDA) to uncover trends, anomalies, and opportunitie sDevelop and validate machine learning models (regression, classification, clustering, etc. )Conduct feature engineering, hypothesis testing, and model evaluatio nTranslate business requirements into data science use cases and analytical solution sDeliver actionable insights to drive optimisation, efficiency ...

limitations, and model performance. Build and productionize analytics that advance intraday/EoD automation (services, APIs, pipelines) with clear SLOs/SLA, observability, reliability engineering practices, and tight integration into trading/risk platforms. Provide technical leadership and mentorship; conduct code/method reviews, establish research engineering best … resourcing via data driven analysis, scenario studies, and clear articulation of trade offs. Lead development of ML/AI solutions end to end (feature engineering, model training/validation, MLOps, monitoring and drift management) with rigorous controls, documented governance, and demonstrable business value. Uphold standards for documentation, reproducibility ...

This is a senior individual contributor role with strong influence through mentorship, collaboration, and technical leadership. You will work closely with partners across product, engineering, and the business to design and deliver data‐driven solutions.We welcome candidates from a range of backgrounds and experiences who are excited to apply … Development** Lead by example through hands-on coding and model development. Design, prototype, and validate innovative solutions for high-impact problems. Partner with engineering teams to deploy models into production environments.* **Domain Expertise** Leverage deep knowledge of aviation, travel, or related industries to inform modeling approaches and deliver customer ...

communication and collaboration skills, with demonstrated experience working across cross-functional teams\ N\nEducation\n\nBachelor’s degree or higher in Computer Science, Software Engineering, or a related field.\ N\nResponsibilities\ NAs a Software Engineer specializing in AI systems, you will play a key role in designing and implementing … agents using techniques such as retrieval-augmented generation, prompt optimization, fine-tuning, and reinforcement learning\ NDevelop end-to-end pipelines for data ingestion, feature engineering, model inference (batch and real-time), and integration of AI-driven workflows into production systems\ NCollaborate with data scientists and machine learning engineers ...

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. … with a team retrospective. Global OTT Technology Our team develops and supports market-leading video streaming services, underpinned by state-of-the-art engineering principles. We do this at huge scale: for over 50 million customers globally, spanning NBCUniversal Peacock in the US and Sky, NOW and SkyShowtime across ...

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. … with a team retrospective. Global OTT Technology Our team develops and supports market-leading video streaming services, underpinned by state-of-the-art engineering principles. We do this at huge scale: for over 50 million customers globally, spanning NBCUniversal Peacock in the US and Sky, NOW and SkyShowtime across ...

collaboration skills, with demonstrated experience working across cross-functional teams \n\n Education \n \n Bachelor’s degree or higher in Computer Science, Software Engineering, or a related field. \n\n Responsibilities \n As a Software Engineer specializing in AI systems, you will play a key role in designing … agents using techniques such as retrieval-augmented generation, prompt optimization, fine-tuning, and reinforcement learning \n Develop end-to-end pipelines for data ingestion, feature engineering, model inference (batch and real-time), and integration of AI-driven workflows into production systems \n Collaborate with data scientists and machine ...

Descriptionhackajob is collaborating with Simply Business to connect them with exceptional professionals for this role.\ NSenior Analytics Engineer - Analytics Engineering\ N \nOur story - continued commitment to doing the right thing\ N \n2005seems like a long time ago. Simply Business – a small company with a big idea. To transform … Simply Business. DNA has been central to empowering data-driven decision making at SB. We have invested heavily in our data pipeline, data engineering, business intelligence and machine learning capabilities. \ N \nThe Senior Analytics Engineer is a senior technical role responsible for thedesign and development of robust analytical data ...

manipulation, analysis, and modelling Experience working with large and complex datasets, such as transactional or behavioural data Good understanding of data modelling concepts and feature engineering techniques Exposure to cloud platforms, ideally Azure, and experience with Databricks is beneficial Confident communicating insights to stakeholders and presenting to clients ...

JDBC and database internals. \n Experience with CI/CD pipelines and Distributed and Concurrent Systems \n Experience with machine learning basics (data pipelines, feature engineering, recall/precision, familiarity with machine learning systems in production) is a huge plus \n A strong product mindset and passion … practical guide \n DEI @ Wise \n Wise Tech Stack (2025 update) \n See what it's like to work at Wise London! \n Our Engineering career map \n\n Wise Engineering – \n What do we offer: \n \n Starting salary: £87,500 - £111,000 + RSUs \n Wise ...

focus on data and digital capability. You'll sit within a dedicated data function, working closely with a Senior Data Scientist and collaborating across engineering, product and business teams to build models that get used, not just presented. Responsibilities as a Data Scientist: Conduct exploratory data analysis to identify … trends, patterns and anomalies across large datasets Build, refine and deploy machine learning models to solve business problems Perform data cleaning, feature engineering and transformation to ensure data quality and model suitability Support integration of models into business processes and operational systems Monitor deployed model performance and recommend ...

teams to align architecture with business objectives. Provide technical leadership and governance across AI work streams and delivery teams Drive best practice in AI engineering, model management, and data science operations Evaluate and select appropriate AI frameworks, tools, and cloud services to meet enterprise requirements Support the integration … systems Skills and Experience Proven experience delivering AI solutions at enterprise scale in production environments Strong background in Generative AI — including LLMs, prompt engineering, and model fine-tuning Hands-on experience with Hugging Face (model hub, Transformers, Inference API) Experience with AWS AI services (SageMaker, Bedrock, Rekognition, Comprehend ...

business and regulatory challenges .Collaborate with data scientists, engineers, and business stakeholders to build and deploy production-grade machine learning models .Perform data analysis, feature engineering, model training, validation, and optimization .Conduct exploratory data analysis (EDA) to identify patterns, anomalies, and emerging risk indicators .Develop and maintain classification … s.Experience wit h time-series analys is to identify trends and assess risks over tim e.Expertise i n exploratory data analysis (ED A) and feature engineerin g.Strong problem-solving skills and the ability to communicate complex analytical findings to technical and non-technical stakeholder s. ...

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 ...

experimentation strategy for the product area — designing A/B tests and multi-armed bandit experiments that generate genuine learning about AI feature performance, not just validate existing assumptions Drive the feedback loop strategy — defining how member behaviour is captured, modelled, and fed back into AI ranking systems … context, using results to change direction rather than confirm instinct Technically credible AI and ML fluency — able to read a model card, challenge a feature engineering decision, write testable acceptance criteria for ML features, and explain confidence intervals to non-technical stakeholders, without overstepping into engineering decisions ...

major growth phase and help build the firm's long-term technology platform.The company combines the stability of an established investment business with the engineering culture of a high-performing technology organization. Engineers work in small, highly autonomous teams with significant ownership over critical systems. Senior hires are expected … data to trading infrastructure, where speed and reliability are fundamental requirements Building integrated environments where quantitative researchers develop, test, and deploy predictive models - from feature engineering through execution Architecting cloud-based simulation and experimentation platforms that support large-scale quantitative research without becoming a bottleneck Standing up toolchains ...

models (e.g., clustering, regression, predictive analytics) with ability to take problems from framing through to deployment-ready outputs Must be strong in data exploration, feature engineering, model evaluation, and communicating insights clearly to non-technical stakeholders ideally within demand-led public services (predictive demand, prevention, triage/segmentation ...

real-time capability Strong understanding of how to assess and enable data readiness for AI use cases, including data availability, data quality and feature engineering considerations Experience analysing decision-making workflows, identifying where data should better inform operational and strategic decisions Ability to establish and promote canonical data ...

will be responsible for designing, building, and governing predictive models that support data-driven products and strategic decision-making. You will work closely with engineering and product teams to transform raw data into robust, auditable, and commercially valuable solutions. Skills & Experience Technical Expertise Strong proficiency in Python (Pandas, NumPy … Snowflake or similar cloud-based data warehouse platforms. Experience using Git and reproducible development practices. Strong understanding of data pipelines, data quality, and analytics engineering principles. Credit Modelling & Predictive Analytics Proven experience conducting rigorous exploratory data analysis and feature engineering. Experience building predictive models in regulated financial services ...