Data Scientist - LDN

Data Scientist - LDN

Full-Time 55000 - 65000 £ / year (est.) Home office (partial)
Third Bridge

At a Glance

  • Tasks: Design and build innovative data solutions using cutting-edge technology.
  • Company: Join Third Bridge, a global leader in research and insights.
  • Benefits: Enjoy 25 days vacation, health insurance, and a £1000 personal development allowance.
  • Other info: Flexible work options and a commitment to diversity and inclusion.
  • Why this job: Make a real impact with your data skills in a dynamic team environment.
  • Qualifications: Masters in a quantitative field and strong Python programming skills required.

The predicted salary is between 55000 - 65000 £ per year.

Third Bridge is a leading global research firm established in 2007, with a team of over 1,500 employees worldwide dedicated to fueling decisions with expert insights. We accelerate and enhance decision‑making for investors and business leaders by unearthing unique expert insights across multiple sectors, geographies, and topics. For nearly 20 years, we’ve helped clients access knowledge on demand from experts, in‑person and through our Library covering over 65,000 companies. We stand at the cutting edge of technology and investment research, driving innovation to deliver solutions that set new industry standards.

We are building a Data Science capability within our Data Architecture function, and are seeking a Data Scientist who combines technical rigour with a prototyping mindset — someone dedicated to proving what’s possible with our data and handing those proofs to engineering.

About the Role: As a Data Scientist at Third Bridge, you will be the engineering‑focused prototyping engine within a high‑impact data team. Working alongside our senior analytics capability and reporting to the Principal Data Architect, your primary mandate is to design, build, and validate proof‑of‑concept (PoC) solutions — from new dataset pipelines and ML models to AI‑powered internal tools — and hand off successful proofs to the engineering team for production integration. You will work with Third Bridge’s rich proprietary data assets: transcripts, events, interaction data, commercial performance metrics, and content derived from tens of thousands of expert interviews. The role gives you the mandate to find new ways to extract value from that data, with the freedom to experiment and the expectation to ship.

Key Responsibilities:

  • Design and build PoC solutions with a clear ‘ship-or-kill’ decision framework, defining evaluation criteria upfront so success is measurable, not just qualitative.
  • Build extraction, transformation, and loading (ETL/ELT) pipelines to create net‑new datasets for analytical or ML use, including feature engineering pipelines for model training and downstream reporting.
  • Apply supervised and unsupervised ML techniques to commercially relevant problems: usage propensity, client segmentation, churn modelling, content recommendation, and anomaly detection.
  • Develop lightweight Python‑based tools and notebook applications that allow business stakeholders to interact with model outputs or curated data extracts.
  • Prototype AI‑assisted internal tools — including applications leveraging LLM APIs, embedding‑based search, and retrieval‑augmented generation (RAG) — to demonstrate near‑term business value from Third Bridge’s content assets.
  • Collaborate closely with the analytics peers to align on data definitions, shared datasets, and metric standards that underpin both experimental and production work.
  • Work with the engineering team to define production requirements for successful PoCs, producing clean, well‑documented code and architecture notes to support seamless handoff.
  • Advocate upstream for data quality and instrumentation requirements with Product and Engineering, contributing to the team’s engineering standards and practices.

Essential Qualifications:

  • Masters degree (or equivalent) in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a quantitative field such as Economics.
  • Professional experience in a data science, ML engineering, or applied data role.
  • Strong Python programming skills, including hands‑on experience with pandas, scikit‑learn, and at least one ML or deep learning framework.
  • Working knowledge of Bedrock and other AWS managed services.
  • Demonstrable experience building and deploying at least one end‑to‑end ML model or data pipeline in a professional setting, with clear evaluation criteria and documented outcomes.
  • A track record of building PoC or prototype solutions and iterating toward production‑readiness, with the discipline to define ‘ship‑or‑kill’ criteria upfront.
  • Strong version control practices (Git) and familiarity with software engineering workflows including code review and CI/CD awareness.
  • The ability to communicate technical findings and trade‑offs clearly to both technical peers and non‑technical stakeholders.

Qualifications Desired Attributes:

  • Exceptional problem‑solving instincts and a bias toward shipping: able to make a PoC useful and demonstrable quickly, without over‑engineering early‑stage solutions.
  • Familiarity with AWS Bedrock or other managed AI/LLM platforms (e.g. OpenAI API, Anthropic API) for building and integrating AI‑powered features is a strong plus, though AI capability should complement, not replace, a solid foundation in traditional ML.
  • Experience with NLP, text classification, embedding models, or semantic search, particularly applied to content‑rich or document‑heavy datasets.
  • Familiarity with building lightweight web applications or data tools such as Streamlit, FastAPI, or Flask, enabling business stakeholders to interact directly with model outputs.
  • Experience with orchestration or transformation tooling such as dbt, Airflow, or Prefect, demonstrating an ability to build reproducible and maintainable pipelines.
  • A tech‑agnostic mindset, open to selecting the right tool for each problem — whether that is a classical statistical model, a gradient boosting approach, or an LLM‑backed workflow.
  • Exposure to product analytics, B2B SaaS, publishing, or content‑rich datasets is a plus.
  • Natural curiosity and a proactive approach to staying current with developments in both traditional ML and generative AI — and bringing relevant ideas back to the team.
  • Excellent collaboration skills, with the instinct to treat the analytics peer and engineering team as partners rather than handoff recipients.

How will you be rewarded?

We truly care about our people so in return for your work, you’ll get:

  • Vacation: 25 days (which increases to 28 days after 2 years of service) plus UK Bank Holidays
  • Learning: personal development allowance of £1000 per year
  • Health and wellbeing: private medical insurance and healthcare cash plan, a variety of health and wellbeing events to focus on mental health, Ride to Work scheme (savings on bikes and accessories)
  • Future and family: pension contributions of 4% (increases with tenure) and life insurance of 4x of your base salary
  • Flexibility: work from anywhere for one month per year, 2 annual volunteer days, 2 personal days when life throws you a curveball and 'Summer Fridays'
  • Rewards: get points through our colleague‑to‑colleague recognition programme to spend on hotels, gift cards, donations to charity and more
  • Social: optional social gatherings, daily breakfast and snacks, social events
  • ESG: CSR, Environment and Diversity & Inclusion (including Women at Third Bridge, Pride and Blkbridge)
  • Frontline Innovation: your chance to share your ideas for improvement through Hackathons and other events

We know that to be truly innovative, we need to have a diverse team around us. That is why Third Bridge is committed to creating an inclusive environment and is proud to be an equal opportunity employer. If you are not 100% sure if you are right for the role, please apply anyway, and we will be happy to consider your application.

Data Scientist - LDN employer: Third Bridge

At Third Bridge, we pride ourselves on being an exceptional employer that fosters a culture of innovation and collaboration. As a Data Scientist in London, you'll enjoy a supportive work environment with ample opportunities for personal and professional growth, including a generous learning allowance and flexible working arrangements. Our commitment to employee wellbeing is reflected in our comprehensive benefits package, which includes private medical insurance, a robust pension scheme, and initiatives that promote diversity and inclusion.
Third Bridge

Contact Detail:

Third Bridge Recruiting Team

StudySmarter Expert Advice 🤫

We think this is how you could land Data Scientist - LDN

✨Tip Number 1

Network like a pro! Reach out to current employees at Third Bridge on LinkedIn, and ask them about their experiences. A friendly chat can give you insider info and might just get your foot in the door.

✨Tip Number 2

Show off your skills! Prepare a mini-project or a proof-of-concept that showcases your data science abilities. Bring it up during interviews to demonstrate your hands-on experience and problem-solving mindset.

✨Tip Number 3

Be ready to discuss your thought process! When you're asked about your past projects, focus on how you approached challenges and made decisions. This will highlight your engineering-focused prototyping mindset.

✨Tip Number 4

Apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining the Third Bridge team.

We think you need these skills to ace Data Scientist - LDN

Python Programming
Machine Learning (ML)
Data Pipeline Development
ETL/ELT Processes
Feature Engineering
Supervised and Unsupervised Learning Techniques
Data Analysis
Prototyping
AWS Managed Services
Version Control (Git)
NLP (Natural Language Processing)
Web Application Development (Streamlit, FastAPI, Flask)
Collaboration Skills
Problem-Solving Skills
Communication Skills

Some tips for your application 🫡

Tailor Your CV: Make sure your CV is tailored to the Data Scientist role at Third Bridge. Highlight your relevant experience, especially in building PoC solutions and working with data pipelines. We want to see how your skills align with our needs!

Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about data science and how you can contribute to our team. Be sure to mention any specific projects or experiences that relate to the job description.

Showcase Your Technical Skills: Don’t forget to highlight your technical skills, especially in Python and ML frameworks. We love seeing examples of your work, so if you have a portfolio or GitHub repository, include that too. It helps us understand your hands-on experience!

Apply Through Our Website: We encourage you to apply through our website for a smoother application process. It’s the best way for us to receive your application and ensures you don’t miss out on any important updates from our team!

How to prepare for a job interview at Third Bridge

✨Know Your Data Science Fundamentals

Make sure you brush up on your data science fundamentals, especially around machine learning techniques and Python programming. Be ready to discuss your experience with building and deploying ML models, as well as any specific projects you've worked on that relate to the role.

✨Prepare for Technical Questions

Expect technical questions that assess your problem-solving skills and understanding of data pipelines. Practice explaining your thought process clearly, especially when discussing how you would approach building a proof-of-concept solution or handling data extraction and transformation.

✨Showcase Your Prototyping Mindset

Third Bridge is looking for someone with a prototyping mindset. Be prepared to share examples of how you've quickly developed PoCs in the past, including the criteria you used to evaluate their success. Highlight your ability to iterate and adapt based on feedback.

✨Communicate Effectively

Since you'll be collaborating with both technical and non-technical stakeholders, practice communicating your ideas clearly. Think about how you can explain complex concepts in simple terms, and be ready to discuss how you've successfully worked with cross-functional teams in previous roles.

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