276 to 300 of 534 Permanent Big Data Jobs

Fintech35,000 + benefitsQuant Capital is urgently looking for a Frontend Developer to join our high profile client.Our client is a brand new investment big social data business. They have created a market leading product in the quantitative asset management market. They combine Big Data Quantitative … will be able to shape this greenfield platform as they like.This role will involve some backend but mostly frontend development of the GUI and Data display for the Quant Team. Current Stack is Javascript with Frameworks with Raw HTML5 CSS3 and Python at the back end.This innovative start ...

productionizing foundation models at scale. Our work centers on self-supervised learning for transformer architectures - pretraining on Capital One's rich, large-scale behavioral data to learn representations that power applications across use cases such as fraud, marketing, and servicing . You'll participate in the detailed technical design … learning, transformer modeling, and ML engineering best practices. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build ...

business-oriented technologists who possess a unique balance of business knowledge, effective interpersonal skills, and some technological depth in the following areas: analytics/big data, machine learning and generative artificial intelligence. This role will focus on supporting customers looking to enhance their business or operational outcomes through … specialist in design and architecture- Experience in Database like NoSQL, or experience in Hive/Spark/Hbase/Yarn and experience in any Bigdata architecture- Bachelor's degree in business administration, finance, economics, computer science, data science, engineering, or other related field, or experience in consulting, design ...

high-performance, high-availability, high-capacity, scalable, near real-time yet nimble and adaptive platform for processing & persisting high volumes of business critical data and distribute it to downstream applications post-trade processing. Develop highly reliable data ingestion processes to consume large volumes of data emitted … trading and market data systems. Design distributed computation infrastructure to run parallelized queries over large volumes of data.Design and develop an AI-powered UI/agent framework that delivers intelligent analytics and insights over the data we process and hold, enabling users to interrogate and derive value from ...

practices in machine learning engineering. What you'll do in the role: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build …/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams. Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training ...

practices in machine learning engineering. What you'll do in the role: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build …/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams. Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training ...

practices in machine learning engineering. What you'll do in the role: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build …/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams. Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training ...

highest leverage impact. What you'll do in the role: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/… deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams. Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning ...

highest leverage impact. What you'll do in the role: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/… deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams. Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning ...

highest leverage impact. What you'll do in the role: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/… deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams. Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning ...

credit journey. Our team standardizes and streamlines how complex machine learning models are built, deployed, and monitored at scale, drastically reducing friction for our data science partners. Utilizing a modern technology stack focused heavily on Python and Kubernetes, you will create high-impact infrastructure that supports millions of customers … from their initial application throughout their entire lifecycle. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build ...

discussed and confirmed with them Create recommendations, project plans, cost estimates, procedures and specifications, ensuring quotes are provided and instructions are agreed in writing Data processing of material received in various formats including native and load file mapping and ingestion, as well as exception handling Setup and customisation … Relativity , running searches and culling data, creating review batches, customising coding templates, creating user roles and related permission settings Carry out native and load file productions according to specifications Resolve 1st line support queries and work with our 2nd & 3rd line support to ensure technical issues are resolved ...

create future-ready solutions that blend strategy, technology, people, and processConduct stakeholder interviews, workshops, and research to shape insights and inform decision-makingApply data-driven thinking and explore emerging technologies to solve complex client challengesParticipate in client presentations, proposals, and business development effortsLearn the scale of our capabilities … leading technology partners to deliver impactWork with innovative technologies and industry-leading methodologies used to solve business problemsEmbrace new ways of working that incorporate data fluency, agile collaboration, and creative problem-solvingWe are looking for the following profiles and skills:Ideal candidates will have experience in one or more ...

create future-ready solutions that blend strategy, technology, people, and processConduct stakeholder interviews, workshops, and research to shape insights and inform decision-makingApply data-driven thinking and explore emerging technologies to solve complex client challengesParticipate in client presentations, proposals, and business development effortsLearn the scale of our capabilities … leading technology partners to deliver impactWork with innovative technologies and industry-leading methodologies used to solve business problemsEmbrace new ways of working that incorporate data fluency, agile collaboration, and creative problem-solvingWe are looking for the following profiles and skills:Ideal candidates will have experience in one or more ...

create future-ready solutions that blend strategy, technology, people, and processConduct stakeholder interviews, workshops, and research to shape insights and inform decision-makingApply data-driven thinking and explore emerging technologies to solve complex client challengesParticipate in client presentations, proposals, and business development effortsLearn the scale of our capabilities … leading technology partners to deliver impactWork with innovative technologies and industry-leading methodologies used to solve business problemsEmbrace new ways of working that incorporate data fluency, agile collaboration, and creative problem-solvingWe are looking for the following profiles and skills:Ideal candidates will have experience in one or more ...

practices in machine learning engineering. What you'll do in the role: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build …/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams. Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training ...

heart of everything we do. A culture of proactiveness and a customer-centric mindset guides us in creating a unified platform that makes data science and analytics accessible to everyone. We aim to inspire our customers to make informed decisions that push their business forward. We provide a user … will be a vital part of this mission, utilising your technical expertise to demonstrate how our Lakehouse Platform can help customers address their complex data challenges. You'll work with a collaborative, customer-focused team who values innovation and creativity, using your skills to create customised solutions to help ...

innovations and best practices in machine learning engineering. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build …/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training ...

practices in machine learning engineering. What you'll do in the role: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build …/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams. Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training ...

practices in machine learning engineering. What you'll do in the role: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build …/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams. Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training ...

practices in machine learning engineering. What you'll do in the role: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build …/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams. Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training ...

practices in machine learning engineering. What you'll do in the role: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build …/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams. Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training ...

capabilities that exemplify those practices. What you'll do in the role: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build …/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams. Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training ...

pipeline development using GitLab CI Experience with monitoring, logging and alerting tools Understanding of secure DevOps practices in regulated environments Experience working with large data stores or big data platforms Desirable skills: Experience supporting AI or data driven platforms Knowledge of cyber security or fraud prevention … within government or critical national infrastructure environments About Peregrine We build workforces that deliver tech and change programmes at leading UK organisations. By combining data science from Peregrine Intelligence, our industry-accredited Peregrine Academy, and market-leading attraction and diversity initiatives, we bridge capability gaps at all levels ...

will be responsible for designing, developing, and implementing DevOps practices for database management systems. This role involves close collaboration with software engineering, operations, and data management teams to ensure seamless deployment, scaling, and monitoring of our database infrastructure. You will bring a problem-solving mindset and a collaborative spirit … compliance standards are met for database systems. Continually improve processes and tools to increase efficiency and reliability of database operations. Manage backup, recovery, and data archival processes. Support and participate in all phases of the software development life cycle, including requirements analysis, design, implementation, integration, and test. Write well ...