Engineering Manager - Applied AI & Data Science

Technology, Product & Data

Our team of 450 colleagues across Technology, Product and Data is at the heart of The Economist Group’s digital-first agenda. Together, we design, build and optimise the digital products, platforms and data capabilities that power our business and reach millions of people around the world. Whether you're an engineer, product manager, designer, data scientist or analyst, you'll work on products that have real-world impact. From enhancing economist.com and developing new products for Economist Intelligence to creating personalised experiences and applying AI to solve complex challenges, you'll help shape how consumers, businesses and government leaders access trusted insights. Working in cross-functional teams, we combine engineering excellence, product thinking and data expertise to solve meaningful problems, experiment with new technologies and continuously improve the experiences we create. It's an opportunity to do challenging, purposeful work alongside curious, collaborative colleagues, while helping drive sustainable growth for one of the world's most trusted brands.

The Role

Our AI Platform team builds the technology that powers AI-enabled products and services, for internal users and external customers. We design, build and operate the infrastructure, patterns and reusable capabilities that enable generative and agentic AI, natural language processing, predictive analytics and machine learning across the Group, from customer-facing features in our digital products to the internal tools used by colleagues across our four businesses.

Scope

We are seeking an Engineering Manager to lead our Applied AI and Data Science team: a multidisciplinary group of AI Engineers, ML Engineers, NLP Engineers and Data Scientists. This is a player-coach role. You will own the health, delivery and growth of the team while remaining a hands-on contributor to its work, writing and reviewing code, shaping architecture and stepping into implementation when it matters.

The team’s work spans generative and agentic AI systems built on large language models, alongside the NLP and predictive models that sit behind our products and internal tools. You will turn the Group’s AI ambitions into well-architected, production-grade systems, working in close partnership with the Head of AI and Data Engineering, product managers, data engineers and business stakeholders to decide what we build, why we build it, and how we know it is working. You will also champion the team’s move to AI-native engineering, with coding agents used across the whole software development lifecycle.

Accountabilities

• Lead and manage the team. Provide day-to-day management, mentorship and career development, and build one team across engineering and data science disciplines.
• Own delivery. Own planning, resourcing, prioritisation and delivery with product management, balancing exploratory work against production commitments.
• Communicate progress. Keep leadership and stakeholders informed on progress, risks and trade-offs, and set realistic expectations of what AI can deliver.
• Stay hands on. Contribute directly to the team’s deliverables: build, prototype, review code and models, and step into implementation when it matters.
• Contribute to technical and architectural decisions. Shape the architecture of our agentic AI, RAG, NLP and ML systems, covering agent design, model selection and build-versus-buy.
• Oversee the development of safe and reliable agents. Ensure our agents have well-scoped tool permissions, guardrails and human-in-the-loop controls, and are evaluated end to end before and after release.
• Ensure quality, security and compliance. Set standards for code quality, testing, evaluation, observability and cost, and keep AI systems secure, well-architected and compliant.
• Champion AI-native engineering. Embed AI coding agents across the SDLC of the team, from specification and design to code, tests and review, with clear standards for AI-generated code.
• Partner with others. Work with Product, Data Engineering, Security, Legal and the business units, challenging requirements and using metrics to define what “done” looks like.
• Hire and grow the team. Recruit, interview and onboard engineers and data scientists, shaping the team’s skills mix to match the AI roadmap.
• Drive best practices and ways of working. Improve CI/CD, experiment tracking and the path to production, and promote the team’s capabilities across the Group.

Selection criteria

Essential

• A track record as an engineering manager or tech lead with direct people-management responsibility for AI/ML engineers, NLP engineers and/or data scientists.
• Experience balancing hands-on technical contribution with people leadership and delivery ownership.
• Strong hands-on AI/ML engineering background, with extensive Python experience and confidence reviewing code across LLM, agentic, NLP and classical ML systems.
• Hands-on experience with modern agent frameworks such as Strands Agents, LangGraph or the Claude Agent SDK, and with interoperability standards such as MCP and A2A.
• Strong cloud-native expertise on AWS, especially Amazon Bedrock and Bedrock AgentCore, alongside SageMaker, Lambda and Step Functions, with CDK or equivalent IaC.
• Hands-on LLMOps and MLOps experience, including agent and trajectory evaluation, tracing (e.g. OpenTelemetry), guardrails, cost monitoring and automated model pipelines.
• A clear understanding of agent security risks, such as prompt injection, data leakage and over-permissioned tools, and how to mitigate them.
• Day-to-day experience of AI-native software delivery, using coding agents such as Claude Code, Cursor or GitHub Copilot across planning, coding, testing and review.
• Able to set team standards for AI-assisted development, including context engineering for coding agents, review of AI-generated code and measuring its impact on delivery.
• A solid grounding in applied data science, enough to lead data scientists and constructively challenge their work.
• Excellent communication skills, able to work closely with product, design and engineering, and explain technical trade-offs to non-technical stakeholders.
• Strong product sense: comfortable interrogating and refining requirements rather than taking them as given, and able to spot when one doesn’t hold up.

Desirable

• Direct experience applying AI within a media, publishing, entertainment or other content-led industry.
• Proven experience building and operating agentic AI systems in production, including multi-agent orchestration, tool use via MCP, agent memory and agentic RAG.Depth in NLP, such as information extraction, classification, summarisation and semantic search, or fine-tuning LLMs.
• Experience building shared AI platforms or reusable capabilities consumed by multiple product teams, such as internal MCP servers or agent tooling.
• Experience with vector search at scale, using tools such as OpenSearch, Amazon S3 Vectors or pgvector.
• Familiarity with AI governance and regulation, and with putting responsible AI practices into day-to-day engineering.
• An advanced degree in computer science, data science, linguistics or a related quantitative field, or equivalent work experience.

Please note: this role is open to internal applicants only until Tuesday, 13 October 2026, after which it may be advertised externally.

Enterprise Sales, B2C and central functions - to identify high-impact opportunities, redesign workflows and embed new, AI-enabled ways of working at scale. 

As part of this AI Transformation Programme, we are hiring an AI Transformation Lead for Editorial to help turn that ambition into measurable results in The Economist’s newsroom. This is a hands-on transformation role for people who combine strong stakeholder skills with practical fluency in building with AI.

You will work closely with our editorial teams to understand how work is done today, identify where AI can make a meaningful difference, shape the right solution and lead it through implementation and adoption. You will be comfortable getting close to the technology yourself, but equally effective at bringing together editorial as well as business, product and engineering colleagues to deliver sustainable change.

This is not a role focused on automating what our journalists do –The Economist will remain a quintessentially human product. You will help prioritise opportunities that allow the newsroom to function more efficiently, take successful initiatives into day-to-day use and track the benefits they deliver.

Key Responsibilities:

• Work within Editorial and partner with other business areas (e.g. Finance, EIU Research & Analysis, Enterprise Sales, B2C, Back-office) to identify, assess and prioritise AI opportunities.
• Understand current editorial workflows and reimagine them, rather than simply digitising existing processes.
• Define problem statements, scope, success measures, delivery approaches and high-value use cases; park (or give back for self-implementation) low-value cases early
• Build working prototypes, models, agents, integrations and get something testable in front of users quickly.
• Take proven use cases through to production, using the necessary resources (self-built or working with engineering and platform teams)
• Drive adoption with the business owner through training, communications, embedding into daily workflow.
• Track usage, outcomes and realised benefit; use evidence to refine, scale or stop initiatives.
• Report progress, benefit and blockers to the Editor and the Executive Editors
• Stay current on AI tooling and translate new developments into AI opportunity areas and judge honestly where AI does not fit.

Key Skills and Competencies:

Essential:

• Vitally, we are looking for candidates with a deep passion and interest in AI as a field, who can demonstrate curiosity about how emerging AI capabilities can be applied responsibly and effectively within Editorial. Experience working with an Editorial team/department is vital for this role.
• 4+ years’ experience in transformation, consulting, product, operations, business analysis, technology delivery or a similar cross-functional role.
• Practical experience applying AI, automation, data or digital tools to improve business processes. This could include prototyping, configuring platforms, shaping delivery or advising teams on implementation.
• Meaningful, demonstrable hands-on experience building with AI and agentic tools.
• Commercial judgement: able to assess value, make trade-offs and focus effort on opportunities with meaningful impact.
• Strong ability to understand operational processes, identify root causes and redesign workflows around user and business needs.
• Comfortable leading discovery activities to understand user needs, uncover opportunities and define target-state processes.
• Experience supporting the adoption, enablement and embedding of new technical capabilities and tools into users’ daily workflows. You care whether what you build gets used.
• Confident working with senior stakeholders, able to influence decisions and bring clarity where priorities or expectations differ.

Also useful:

• Experience taking AI-enabled products, automations or services into production, including evaluation, monitoring, governance and cost management
• Data wrangling and engineering - experience working with messy data spread across multiple systems
• Experience in media, publishing, research, subscription businesses or professional services

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