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

Apply Now

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

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.

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.