Product Data Scientist (London or New York)

TechChain Talent
London
1 month ago
Applications closed

Related Jobs

View all jobs

Product Data Scientist

Senior Data Scientist (GenAI)

Data Scientist

Lead Data Analyst

Data Science Product Intern (PhD Level) - Tesco

Data Science Product Intern (PhD Level)

Product Data Scientist -On-site |London or New York | Up to $350k

We're looking for a Product Data Scientist to join a fast-moving team building one of the most data-driven products in Web3.


You'll work on-site with engineers, PMs, and designers to shape product direction through experimentation, behavioural analysis, and deep product insights. This isn't a back‑office analytics role; your work will directly influence how millions of users engage with a rapidly growing on‑chain platform.


What You'll Do

  • Design and analyze A/B tests and product experiments to guide feature development and growth.
  • Define key product metrics and success indicators with Product and Engineering teams.
  • Build and maintain dashboards and data pipelines to monitor performance and retention.
  • Conduct deep‑dive analyses into user and token behaviour to uncover growth opportunities.
  • Help shape an internal experimentation and insights framework that drives faster iteration and smarter decisions.

What Were Looking For

  • 5+ years in Data science, experimentation, or growth analytics.
  • Expert in SQL and Python (or R).
  • Proven track record running and interpreting A/B tests and causal inference studies.
  • Strong understanding of funnel metrics, cohort analysis, and retention modelling.
  • Excellent communication and storytelling able to turn complex data into actionable insights.
  • Excited about Web3, experimentation, and building at speed.

Why This Role

  • On-site only: London or New York we believe in collaboration, creativity, and fast feedback loops.
  • Compensation up to $350K
  • Work in a small, high-impact team shaping the data and experimentation strategy behind one of the fastest-growing platforms in Web3.

If you're a data scientist who thrives on experimentation, loves building from first principles, and wants your work to drive product decisions wed love to talk. For more information please email:


#J-18808-Ljbffr

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 Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

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

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

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

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.