376 to 400 of 922 Machine Learning Jobs in London

still being hands on with technology. We aim to allow growth without becoming “post-technical”. Responsibilities: Collaborate with Data Scientists and Engineers to deliver production quality AI and Machine Learning systems Build frameworks and supporting tooling for data ingestion from a complex variety of sources Consult with our clients on data strategy, modernising their data infrastructure, architecture More ❯

still being hands on with technology. We aim to allow growth without becoming “post-technical”. Responsibilities: Collaborate with Data Scientists and Engineers to deliver production quality AI and Machine Learning systems Build frameworks and supporting tooling for data ingestion from a complex variety of sources Consult with our clients on data strategy, modernising their data infrastructure, architecture More ❯

still being hands on with technology. We aim to allow growth without becoming “post-technical”. Responsibilities: Collaborate with Data Scientists and Engineers to deliver production quality AI and Machine Learning systems Build frameworks and supporting tooling for data ingestion from a complex variety of sources Consult with our clients on data strategy, modernising their data infrastructure, architecture More ❯

still being hands on with technology. We aim to allow growth without becoming “post-technical”. Responsibilities: Collaborate with Data Scientists and Engineers to deliver production quality AI and Machine Learning systems Build frameworks and supporting tooling for data ingestion from a complex variety of sources Consult with our clients on data strategy, modernising their data infrastructure, architecture More ❯

analysis & tools (essential) Predictive modelling (e.g. logistic regression) (essential) Choice/Allocation based models (essential) Decision/Regression based trees (essential) Key Driver analysis methods (essential) Multivariate analysis (essential) Machine learning/AI methods (essential) Experimental design (desirable) Bayesian methods (desirable) Time series analytics (desirable) Text analytics (desirable) Data science best practices (e.g. version control, scripts & libraries, reproducibility More ❯

to learn. APIs: Proficient in working with HTTP APIs AI: An understanding of LLMs, how they can be used as an aid to automation, and/or how the machine learning/fine-tuning process works. Generalist: Not afraid to get stuck-in to any part of the team to ensure project delivery. Ticket Analysis and Resolution: Able More ❯

to interpret data for strategic decision-making. Sales & Revenue Operations: Experience in CRM management (e.g., Salesforce, MS Dynamics) and sales process optimisation. AI & Emerging Technologies: Understanding of Generative AI, machine learning concepts, and automation tools. Leadership & Soft Skills Strategic leadership with ability to set vision and influence stakeholders at all levels. Senior stakeholder management and vendor negotiation skills. More ❯

to interpret data for strategic decision-making. Sales & Revenue Operations: Experience in CRM management (e.g., Salesforce, MS Dynamics) and sales process optimisation. AI & Emerging Technologies: Understanding of Generative AI, machine learning concepts, and automation tools. Leadership & Soft Skills Strategic leadership with ability to set vision and influence stakeholders at all levels. Senior stakeholder management and vendor negotiation skills. More ❯

to interpret data for strategic decision-making. Sales & Revenue Operations: Experience in CRM management (e.g., Salesforce, MS Dynamics) and sales process optimisation. AI & Emerging Technologies: Understanding of Generative AI, machine learning concepts, and automation tools. Leadership & Soft Skills Strategic leadership with ability to set vision and influence stakeholders at all levels. Senior stakeholder management and vendor negotiation skills. More ❯

to interpret data for strategic decision-making. Sales & Revenue Operations: Experience in CRM management (e.g., Salesforce, MS Dynamics) and sales process optimisation. AI & Emerging Technologies: Understanding of Generative AI, machine learning concepts, and automation tools. Leadership & Soft Skills Strategic leadership with ability to set vision and influence stakeholders at all levels. Senior stakeholder management and vendor negotiation skills. More ❯

goals. About You Must-Haves 5+ years in data science, analytics, or similar roles, with at least 2 years in fintech, payments, or financial services. Solid grounding in statistics, machine learning, and data engineering principles. Experience with Python (e.g., pandas, scikit-learn, seaborn) and SQL (Postgres preferred) for advanced data manipulation. Familiarity with the modern data stack (Databricks More ❯

new features or fixes meet business requirements Documentation: Create clear, detailed documentation including process flows, use cases, user stories, and training materials AI: Champion the use of AI and machine learning to automate time-consuming media processes - e.g., audience insights Monetization: Understanding of media monetization strategies (e.g., ad-based, subscription, licensing) Collaboration: Work closely with senior leadership to More ❯

they meet the needs of an expanding customer base. Lead product roadmap initiatives that improve the accuracy, efficiency, and relevance customer screening. Collaborate with Technology teams to enhance automation, machine learning, and NLP (natural language processing) and generative AI capabilities. Develop and maintain strong relationships with customers, regulators, and industry partners to anticipate compliance challenges and evolving risk More ❯

and capabilities that these typically solve for Familiarity with one or more of the hyperscale cloud platforms (Azure, AWS, GCP) Knowledge of emerging efficiency driving technologies such as AI, Machine Learning, and/or intelligent automation Proven aptitude in business development , encompassing account planning, pitches, and proposals. Your Key Abilities: Exceptional interpersonal skills spanning organizational levels, including ability More ❯

and capabilities that these typically solve for Familiarity with one or more of the hyperscale cloud platforms (Azure, AWS, GCP) Knowledge of emerging efficiency driving technologies such as AI, Machine Learning, and/or intelligent automation Proven aptitude in business development , encompassing account planning, pitches, and proposals. Your Key Abilities: Exceptional interpersonal skills spanning organizational levels, including ability More ❯

and capabilities that these typically solve for Familiarity with one or more of the hyperscale cloud platforms (Azure, AWS, GCP) Knowledge of emerging efficiency driving technologies such as AI, Machine Learning, and/or intelligent automation Proven aptitude in business development , encompassing account planning, pitches, and proposals. Your Key Abilities: Exceptional interpersonal skills spanning organizational levels, including ability More ❯

and capabilities that these typically solve for Familiarity with one or more of the hyperscale cloud platforms (Azure, AWS, GCP) Knowledge of emerging efficiency driving technologies such as AI, Machine Learning, and/or intelligent automation Proven aptitude in business development , encompassing account planning, pitches, and proposals. Your Key Abilities: Exceptional interpersonal skills spanning organizational levels, including ability More ❯

tax incentive schemes Degree in Computer Science, Software Engineering, or related field (BSc/MSc/PhD) Proficiency in modern programming languages, software architecture, cloud platforms, databases, AI/ML frameworks, DevOps practices, and cybersecurity Excellent communication, organisational, and listening skills High business integrity and ethics Benefits: Competitive salary range of £60,000 - £65,000 Exposure to a diverse client More ❯

with asynchronous programming (e.g., asyncio, Celery). Exposure to message brokers like RabbitMQ, Kafka. Understanding of GraphQL, gRPC, or WebSockets. Previous experience in architecting data-intensive or AI/ML-based systems. Certifications in cloud technologies or architecture (AWS/Azure Architect, TOGAF, etc.). Opportunities & Application This is an excellent opportunity on a great project of work. If you More ❯

Integrate data from enterprise systems (e.g. Dynamics, iTrent, Unit4) into a unified data platform. Cleanse, transform, and model data to support BI tools (e.g., Power BI) and AI/ML use cases. Implement data validation, lineage tracking, and metadata tagging for observability and trust. Collaborate with stakeholders to standardise KPIs and support cross-functional reporting. Ensure secure handling of sensitive More ❯

Integrate data from enterprise systems (e.g. Dynamics, iTrent, Unit4) into a unified data platform. Cleanse, transform, and model data to support BI tools (e.g., Power BI) and AI/ML use cases. Implement data validation, lineage tracking, and metadata tagging for observability and trust. Collaborate with stakeholders to standardise KPIs and support cross-functional reporting. Ensure secure handling of sensitive More ❯

Integrate data from enterprise systems (e.g. Dynamics, iTrent, Unit4) into a unified data platform. Cleanse, transform, and model data to support BI tools (e.g., Power BI) and AI/ML use cases. Implement data validation, lineage tracking, and metadata tagging for observability and trust. Collaborate with stakeholders to standardise KPIs and support cross-functional reporting. Ensure secure handling of sensitive More ❯

Integrate data from enterprise systems (e.g. Dynamics, iTrent, Unit4) into a unified data platform. Cleanse, transform, and model data to support BI tools (e.g., Power BI) and AI/ML use cases. Implement data validation, lineage tracking, and metadata tagging for observability and trust. Collaborate with stakeholders to standardise KPIs and support cross-functional reporting. Ensure secure handling of sensitive More ❯

Integrate data from enterprise systems (e.g. Dynamics, iTrent, Unit4) into a unified data platform. Cleanse, transform, and model data to support BI tools (e.g., Power BI) and AI/ML use cases. Implement data validation, lineage tracking, and metadata tagging for observability and trust. Collaborate with stakeholders to standardise KPIs and support cross-functional reporting. Ensure secure handling of sensitive More ❯

Integrate data from enterprise systems (e.g. Dynamics, iTrent, Unit4) into a unified data platform. Cleanse, transform, and model data to support BI tools (e.g., Power BI) and AI/ML use cases. Implement data validation, lineage tracking, and metadata tagging for observability and trust. Collaborate with stakeholders to standardise KPIs and support cross-functional reporting. Ensure secure handling of sensitive More ❯