Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Lead Data Scientist - Data Cloud Acceleration

Zeta Global
London
2 months ago
Applications closed

Related Jobs

View all jobs

Lead Data Scientist

Lead Data Scientist

Lead Data Scientist - Remote

Lead Data Scientist - Remote

Lead Data Scientist - Remote

Lead Data Scientist

WHO WE ARE 

Zeta Global (NYSE: ZETA) is the AI-Powered Marketing Cloud that leverages advanced artificial intelligence (AI) and trillions of consumer signals to make it easier for marketers to acquire, grow, and retain customers more efficiently. Through the Zeta Marketing Platform (ZMP), our vision is to make sophisticated marketing simple by unifying identity, intelligence, and omnichannel activation into a single platform – powered by one of the industry’s largest proprietary databases and AI. Our enterprise customers across multiple verticals are empowered to personalize experiences with consumers at an individual level across every channel, delivering better results for marketing programs. Zeta was founded in 2007 by David A. Steinberg and John Sculley and is headquartered in New York City with offices around the world. To learn more, go to .


About the team


We’re a small band of business savvy technologists who treat machine learning as a means, not an end. Our charter: find revenue shaping opportunities, ship the first working model or service in days or weeks, watch how the market reacts, then double down—or pivot—fast. Ego takes a back seat to curiosity, and “good enough for now” often beats “perfect but late.”


What you’ll do

Frame & focus. Translate fuzzy growth ideas into moldeable problems, pick the metrics that matter, and design bite-sized experiments to learn quickly. Build fast, in or out of the box. Finetune a foundation model when it’s the 80percent solution; spin up a from scratch architecture only when the use case truly needs it. Own the full lifecycle. Prototype in notebooks, productionize via Python APIs or lightweight microservices, and wire up offline scoring, real-time inference, and monitoring. Make it self-serve. Wrap models in simple endpoints, SDKs, or SQL functions so analysts and engineers can self select the magic without a helpdesk ticket. Instrument & iterate. Track performance drift, cost, and business lift; retrain or retire ruthlessly based on evidence. Teach the village. Run demos, share code snippets, and mentor teammates on pragmatic ML patterns that survive first contact with customers.

Preferred experience (great to have, but not required)

End to end ML product ownership—from prototype notebook to cloud native service Fluency in Python with libraries such as scikitlearn, PyTorch, TensorFlow, XGBoost, LightGBM Experience choosing and finetuning foundation/LLM or diffusion models when they’re the quickest path to value Comfort with feature stores, vector databases, and MLOps stacks (Airflow/Prefect, MLflow, Kubeflow, SageMaker, Vertex, or equivalents) Both batch and low latency serving patterns (REST, gRPC, or streaming) SQL that hunts for signal in messy data and A/B results Solid grounding in statistics and experimental design, plus the storytelling chops to explain lift to non-data partners Version control, CI/CD, and a bias toward shipping thin vertical slices over monoliths

You’ll thrive here if you…

Think “impact > model elegance.” You pick the simplest approach that moves the KPI. Prototype loudly. You’d rather show a working demo than a 40page deck. Stay humble. If a spreadsheet baseline wins, you celebrate—and then raise the bar. Translate effortlessly. You can chat GPU kernels at noon and revenue funnels at 12:05. Love ambiguity. Blank whiteboards signal possibility, not paralysis.

Why join Zeta’s Data Cloud Acceleration team

Velocity. Your models meet customers in weeks, not quarters. Tool freedom. Choose the stack that fits the problem—no six month procurement saga. Breadth. Projects jump from ad-tech optimization to identity resolution to GenAIpowered personalization. Colleagues who get it. Sharp minds, low egos, and a shared hunger for measurable business results. Global flexibility. Work where you think best—our culture is built for distributed teams.

This is a hybrid role based out of our London, UK office.

SALARY RANGE


The salary range for this role is 75,000 - 85,000 GBP, depending on location and experience. 


PEOPLE & CULTURE AT ZETA

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Why AI Careers in the UK Are Becoming More Multidisciplinary

Artificial intelligence is no longer a single-discipline pursuit. In the UK, employers increasingly want talent that can code and communicate, model and manage risk, experiment and empathise. That shift is reshaping job descriptions, training pathways & career progression. AI is touching regulated sectors, sensitive user journeys & public services — so the work now sits at the crossroads of computer science, law, ethics, psychology, linguistics & design. This isn’t a buzzword-driven change. It’s happening because real systems are deployed in the wild where people have rights, needs, habits & constraints. As models move from lab demos to products that diagnose, advise, detect fraud, personalise education or generate media, teams must align performance with accountability, safety & usability. The UK’s maturing AI ecosystem — from startups to FTSE 100s, consultancies, the public sector & universities — is responding by hiring multidisciplinary teams who can anticipate social impact as confidently as they ship features. Below, we unpack the forces behind this change, spotlight five disciplines now fused with AI roles, show what it means for UK job-seekers & employers, and map practical steps to future-proof your CV.

AI Team Structures Explained: Who Does What in a Modern AI Department

Artificial Intelligence (AI) and Machine Learning (ML) are no longer confined to research labs and tech giants. In the UK, organisations from healthcare and finance to retail and logistics are adopting AI to solve problems, automate processes, and create new products. With this growth comes the need for well-structured teams. But what does an AI department actually look like? Who does what? And how do all the moving parts come together to deliver business value? In this guide, we’ll explain modern AI team structures, break down the responsibilities of each role, explore how teams differ in startups versus enterprises, and highlight what UK employers are looking for. Whether you’re an applicant or an employer, this article will help you understand the anatomy of a successful AI department.

Why the UK Could Be the World’s Next AI Jobs Hub

Artificial Intelligence (AI) has rapidly moved from research labs into boardrooms, classrooms, hospitals, and homes. It is already reshaping economies and transforming industries at a scale comparable to the industrial revolution or the rise of the internet. Around the world, countries are competing fiercely to lead in AI innovation and reap its economic, social, and strategic benefits. The United Kingdom is uniquely positioned in this race. With a rich heritage in computing, world-class universities, forward-thinking government policy, and a growing ecosystem of startups and enterprises, the UK has many of the elements needed to become the world’s next AI hub. Yet competition is intense, particularly from the United States and China. Success will depend on how effectively the UK can scale its strengths, close its gaps, and seize opportunities in the years ahead. This article explores why the UK could be the world’s next global hub for artificial intelligence, what challenges it must overcome, and what this means for businesses, researchers, and job seekers.