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

Apply Now

Head of Data Science Technology (Product, Engineering, Design) · London ·

RedCloud
City of London
21 hours ago
Create job alert
About RedCloud

The global supply chain is broken—creating a $2 trillion inventory gap where essential consumer goods fail to reach the people who need them. Brands miss sales, distributors mismanage stock, and retailers face empty shelves. The result? Higher prices, slower growth, and lost opportunity across the board.


RedCloud is fixing this. Our RedAI digital trading platform, bulk and retail trading exchanges connect key parts of the supply chain—enabling bulk inventory exchange, streamlined digital payments, and generating vast quantities of aggregated market data. By applying AI and machine learning techniques, we deliver predictive market insight and trading recommendations straight back to the trading environment—facilitating smarter everyday business decisions for our customers, from factory to warehouse to store.


Headquartered in London, RedCloud became a publicly listed company on Nasdaq (RCT) in March 2025. With a diverse team spanning many nationalities and operations across Africa, the Middle East, Europe, and Latin America. We’re building a more connected and efficient global trade network. Our AI labs are busy exploring the next generation of smart AI agents and deeper FMCG market intelligence for the benefit of our customers across a growing operational footprint.


The role

The ideal candidate will bring strong leadership experience in data science, strategic vision for data-driven initiatives, and a proven track record of delivering business impact. Key requirements are a deep technical understanding of data science, machine learning, and MLOps, alongside the ability to build and lead teams, communicate complex ideas to stakeholders, and align data strategy with broader business goals.


You will play an integral role, taking ownership of a team focused on designing, developing, and deploying ML systems to solve real world problems and enhance business processes, leveraging cloud platforms to build, train, and scale ML models. As Head of Data Science, you will lead the development and implementation of state-of-the‑art algorithms, including Computer Vision techniques, LLMs and other GenAI models.


Collaborating closely with multiple stakeholders and cross‑functional teams, including Data and Software engineers, to integrate AI solutions into existing systems. Working with Product and business stakeholders to understand challenges and translate them into efficient AI‑driven products aligned with broader business goals. Keeping up with the latest advancements in AI, research new techniques and implement them to enhance the performance and accuracy of solutions.


What You’ll Do

  • Strategic leadership: Develop and implement data science and AI strategies, aligning them with company‑wide objectives.
  • Team leadership: Build, manage, and inspire a high‑performing data science team through setting objectives, performance management, and talent development.
  • Technical direction: Define the technical roadmap, champion best practices, and drive the transition to production‑level machine learning engineering.
  • Stakeholder management: Collaborate with senior leaders and other stakeholders to identify new opportunities and ensure data science solutions meet business needs.
  • Innovation and delivery: Oversee the design, development, and deployment of data science solutions, from research to production, and maintain a focus on innovation.
  • Communication: Translate complex data science concepts into clear, compelling, and user‑friendly information for various audiences, including senior leadership.
  • Governance and ethics: Ensure data governance and ethics are embedded in organizational strategy and champion these principles across the company.

What you’ll need

  • Leadership and management: Significant experience as a senior leader in a data science or AI‑related role, with a track record of managing multi‑disciplinary teams and budgets.
  • Technical expertise: Deep technical knowledge of the entire data science lifecycle, including data engineering, machine learning, and MLOps.
  • Programming and analytics: Proficiency in programming languages such as Python, R, or SQL, and experience with various data science and machine learning libraries.
  • Experience with libraries/frameworks like NumPy, Pandas, SciPy, Scikit‑learn, TensorFlow, PyTorch, Transformers, Langchain, Streamlit, or Gradio, among others.
  • Experience with cloud‑based tools for production use (e.g., AWS SageMaker, AWS Bedrock, Vertex AI, Azure Machine Learning, Azure OpenAI).
  • Knowledge of databases and data technologies, such as Snowflake, BigQuery, and relational databases like SQL.
  • Business acumen: Strong understanding of how to apply data science techniques to solve business problems and deliver tangible value.
  • Communication and influence: Excellent communication skills with the ability to build trust, influence stakeholders, and advocate for data‑driven decisions.
  • Project management: Proven ability to scope, design, implement, and evaluate data science projects to tight deadlines.

Even if you don’t meet every requirement, we still encourage you to apply. Your unique experiences and perspectives might be just what we’re looking for.


Benefits

Working with a pioneering provider of eCommerce solutions you will have the opportunity to join an international company who are growing massively, we encourage ambition and creativity.


Plus, you will get:



  • 25 Days Annual leave, increasing to 26 days after 12 months in the business
  • Enhanced CompanyPension (Matched up to 5% & Salary Sacrifice)
  • Healthcare Cashplan with Medicash
  • Private Healthcare with Aviva
  • Life Insurance with AIG
  • Happl, our benefit platform which provides access to pre‑negotiated discounts on a wide variety of services including entertainment, food, and fitness.
  • Stock / Equity

Check out the links below to see what our CEO Justin Floyd has to say about our plans for growth for the year ahead, and to see our latest video on the mission we’re on!


RedCloud I We're growing!


RedCloud I Red101 App I Open Commerce


#J-18808-Ljbffr

Related Jobs

View all jobs

Head of Data Architecture

Head of Data Architecture

Head of Data Science

Head of Data Architecture

Head of Data Architecture

Head of Data Architecture

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.

Data Science Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK data science hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise rigorous problem framing, high‑quality analytics & modelling, experiment/causality, production awareness (MLOps), governance/ethics, and measurable product or commercial impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for product/data scientists, applied ML scientists, decision scientists, econometricians, growth/marketing analysts, and ML‑adjacent data scientists supporting LLM/AI products. Who this is for: Product/decision/data scientists, applied ML scientists, econometrics & causal inference specialists, experimentation leads, analytics engineers crossing into DS, ML generalists with strong statistics, and data scientists collaborating with platform/MLOps teams in the UK.

Why Data Science Careers in the UK Are Becoming More Multidisciplinary

Data science once meant advanced statistics, machine learning models and coding in Python or R. In the UK today, it has become one of the most in-demand professions across sectors — from healthcare to finance, retail to government. But as the field matures, employers now expect more than technical modelling skills. Modern data science is multidisciplinary. It requires not just coding and algorithms, but also legal knowledge, ethical reasoning, psychological insight, linguistic clarity and human-centred design. Data scientists are expected to interpret, communicate and apply data responsibly, with awareness of law, human behaviour and accessibility. In this article, we’ll explore why data science careers in the UK are becoming more multidisciplinary, how these five disciplines intersect with data science, and what job-seekers & employers need to know to succeed in this transformed field.

Data Science Team Structures Explained: Who Does What in a Modern Data Science Department

Data science is one of the most in-demand, dynamic, and multidisciplinary areas in the UK tech and business landscape. Organisations from finance, retail, health, government, and beyond are using data to drive decisions, automate processes, personalise services, predict trends, detect fraud, and more. To do that well, companies don’t just need good data scientists; they need teams with clearly defined roles, responsibilities, workflows, collaboration, and governance. If you're aiming for a role in data science or recruiting for one, understanding the structure of a data science department—and who does what—can make all the difference. This article breaks down the key roles, how they interact across the lifecycle of a data science project, what skills and qualifications are typical in the UK, expected salary ranges, challenges, trends, and how to build or grow an effective team.