Wave Talent | Data Scientist

Wave Talent
London
2 months ago
Applications closed

Related Jobs

View all jobs

Associate Architect - Up to £62,500 + Bonus

Job Title:Data Scientist (Mid/Senior)

Salary:£85-110k + very generous equity (of the same value as your salary!)

Location:Shoreditch (2 office days/week)

Industry:FinTech / Payments


Having raised over £7m at seed stage, this FinTech company is shaking up the payments space with their innovative real-time price comparison solutions, making card payments a lot fairer and cheaper for smaller businesses and their customers


With a host of very impressive investors backing this venture, and working in collaboration with the world's largest players in payments, this company is causing the biggest change to how businesses access card payments in over two decades.


As the company is gearing up for Series A funding next year, they're looking to set up a Data Science team to leverage a vast amount of data they're collecting from their clients on daily basis and turn this into valuable insights and analytics.


This will be the 1st/founding Data Scientist in the company, with a 2nd hire in this team budgeted for early 2025, and a possible further growth later in the year as the company expands.


✅ Must have requirements:

  • Python
  • REST APIs
  • SQL
  • PostgreSQL
  • Cloud infrastructure - AWS is highly desirable, but GCP and Azure backgrounds are also ok
  • Experience working in a small, fast-growing start-up - ideally a FinTech


Bonus points:

  • Experience with TypeScript, R and React
  • Experience with TensorFlow or other ML libraries
  • Strong academic background


Please note: unfortunately, this roledoes notoffer VISA sponsorship.

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Careers

Tips for Staying Inspired: How Machine Learning Pros Fuel Creativity and Innovation

Machine learning (ML) continues to reshape industries—from personalised e-commerce recommendations and autonomous vehicles to advanced healthcare diagnostics and predictive maintenance in manufacturing. Yet behind every revolutionary model lies a challenging and sometimes repetitive process: data cleaning, hyperparameter tuning, infrastructure management, stakeholder communications, and constant performance monitoring. It’s no wonder many ML professionals can experience creative fatigue or get stuck in the daily grind. So, how do machine learning experts keep their spark alive and continually generate fresh ideas? Below, you’ll find ten actionable strategies that successful ML engineers, data scientists, and research scientists use to stay innovative and push boundaries. Whether you’re an experienced practitioner or just breaking into the field, these tips can help you fuel creativity and discover new angles for solving complex problems.

Careers

Top 10 Machine Learning Career Myths Debunked: Key Facts for Aspiring Professionals

Machine learning (ML) has become one of the hottest fields in technology—touching everything from recommendation engines and self-driving cars to language translation and healthcare diagnostics. The immense potential of ML, combined with attractive compensation packages and high-profile success stories, has spurred countless professionals and students to explore this career path. Yet, despite the boom in demand and innovation, machine learning is not exempt from myths and misconceptions. At MachineLearningJobs.co.uk, we’ve had front-row seats to the real-life career journeys and hiring needs in this field. We see, time and again, that outdated assumptions—like needing a PhD from a top university or that ML is purely about deep neural networks—can mislead new entrants and even deter seasoned professionals from making a successful transition. If you’re curious about a career in machine learning or looking to take your existing ML expertise to the next level, this article is for you. Below, we debunk 10 of the most persistent myths about machine learning careers and offer a clear-eyed view of the essential skills, opportunities, and realistic paths forward. By the end, you’ll be better equipped to make informed decisions about your future in this dynamic and rewarding domain.

Careers

Global vs. Local: Comparing the UK Machine Learning Job Market to International Landscapes

How to evaluate opportunities, salaries, and work culture in machine learning across the UK, the US, Europe, and Asia Machine learning (ML) has rapidly transcended the research labs of academia to become a foundational pillar of modern technology. From recommendation engines and autonomous vehicles to fraud detection and personalised healthcare, machine learning techniques are increasingly ubiquitous, transforming how organisations operate. This surge in applications has fuelled an extraordinary global demand for ML professionals—data scientists, ML engineers, research scientists, and more. In this article, we’ll examine how the UK machine learning job market compares to prominent international hubs, including the United States, Europe, and Asia. We’ll explore hiring trends, salary ranges, workplace cultures, and the nuances of remote and overseas roles. Whether you’re a fresh graduate aiming to break into the field, a software engineer with an ML specialisation, or a seasoned professional seeking your next challenge, understanding the global ML landscape is essential for making an informed career move. By the end of this overview, you’ll be equipped with insights into which regions offer the best blend of salaries, work-life balance, and cutting-edge projects—plus practical tips on how to succeed in a domain that’s constantly evolving. Let’s dive in.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.