Senior Data Scientist

Synthesis
London
1 week ago
Create job alert

At Synthesis we push the boundaries of what’s possible with Open Data. It’s a fast-paced, rapidly-changing environment at the intersection of data science, cultural analysis & brand consultancy.


As a Senior Data Scientist at Synthesis, you will play a key role in shaping our data science practice and driving innovation across both client projects and internal products. You will lead end-to-end data projects, from shaping datasets and building models to designing scalable data workflows that power the insights and decision frameworks we deliver.


You will work with diverse open data sources, ranging from search, social and e-commerce, to academic papers and patents, to understand audience behaviour and anticipate how needs will evolve over time. In this role, you will not only contribute hands-on but also guide junior data scientists, act as a thought partner to cultural strategists, and help define the roadmap for our data products and infrastructure.


We are a small, collaborative team with diverse backgrounds—from computer science and engineering to anthropology and law—spread across Singapore, London and NYC. We value curiosity, creativity, and a spirit of experimentation. You’ll be one of the first hires into our UK office, and you’ll work closely with the teams in Singapore and the US.


What you’ll need.

  • 4+ years of experience in data science, with a strong record of delivering impactful projects from data exploration through to production deployment.
  • Proficiency in Python and SQL, with strong software engineering fundamentals (clean, modular, well-documented code, testing, and version control with Git).
  • Proven experience designing and building scalable data workflows or data-driven products, including architecting pipelines, modular components, and reusable frameworks.
  • Experience applying large language models or advanced NLP techniques (e.g. embeddings, semantic parsing, topic modelling, text classification); familiarity with processing non-english languages is a plus.
  • Comfortable working with large, messy, and unstructured datasets, particularly text and image-based data.
  • Ability to translate complex technical insights into clear, actionable recommendations for non-technical stakeholders, and collaborate closely with strategists to drive business outcomes.
  • A collaborative mindset, with openness to feedback and diverse perspectives.


Bonus points if you have the following.

  • Experience building and maintaining production-grade data pipelines (e.g. Airflow, Dagster) or integrating data from APIs, web scraping, or third-party providers.
  • Familiarity with cloud environments such as Google Cloud or AWS, and deploying data science workflows at scale
  • Strong grasp of statistical methods and core machine learning algorithms, with practical experience using libraries such as scikit-learn, statsmodels, and the ability to select appropriate models based on data characteristics and problem context.


Why Synthesis?

Synthesis is known for delivering exceptional data models and products that unravel the stories of the people they represent and inspire our partners to act with confidence.


Partnering with the world’s most successful brands in food, health, media, and travel, we’re building specialised solutions to solve problems of planning for the future. Rooted in culture, tested in data science, we spot and anticipate changes and connections in culture to inspire action and help brands grow with a new wave of audiences. After five years of iteratively developing and perfecting these models, we are in the midst of launching a series of products which you will be instrumental in shaping.


We will be at the forefront of a shift towards leveraging open data to develop rich, honest, human insight. In an industry that has for too long relied on ‘question-response’ approach to understanding changes in behaviour, we prioritise layering behavioural, performative, search and sales data to highlight the discrepancies between what people say they do and what they actually do. We see a $40 billion industry that has failed to innovate for too long and we are just getting started.


About Synthesis


We do Human Centred Data Science.


It’s a way of reimaging open data sources from a human perspective. It prioritises behaviour and context to unravel why people do what they do, at scale. By blending data science with human and market intelligence we help our clients spot early signals of change and predict implications for business.


Diverse by design.


Synthesis is a team of digital researchers, game designers, data forecasters, network scientists, ethnographers, and engineers. Cultural and category experts train our models – ensuring they detect measures that matter most – whilst data scientists uncover patterns the eye cannot see.

Related Jobs

View all jobs

Senior Data Scientist

Senior Data Scientist (GenAI)

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

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.

How Many AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

What Hiring Managers Look for First in AI Job Applications (UK Guide)

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.