Product Team Lead

Harrington Starr
London
2 months ago
Applications closed

Related Jobs

View all jobs

Lead Developer

Lead Senior Structural Engineer

Lead Senior Structural Engineer

Senior Civil Engineer

ML Team Lead

Senior Software Engineer and Team Leader

Want to work within a team of passionate developers who have the strong belief that putting analytical minds together, you will create the next best generation software?


Join a data analytics scale-up fintech who began their journey just over 8 years ago and have been hugely successful within their market. They are a seeking a unique individual who loves to take ownership of the projects they work on whilst leading a team of strong Python Engineers.


Hybrid working role with flexible working hours based in the City of London.


Be given the opportunity to come in early days within a growing firm and a growing team, working in an open and collaborative environment, bouncing ideas off each other to bring this business on further.


This role involves leading a development team of 8 engineers, working alongside the SaaS applications Product Manager. You will be building AWS cloud-native data science and visualisation applications, using Python, React, Typescript and AWS.


Responsibilities:

  • Lead and manage a development team of Python and Typescript engineers
  • Develop technology roadmaps aligned with business goals
  • Oversee the application design and development, with effective strategies for testability and product quality.
  • Ensure the architecture of the system aligns with best practice and meets the goals for scalability, reliability & security


You will need:

  • Proven experience in managing a development team
  • Expertise in developing single-tenant and multi-tenant B2B SaaS applications
  • Proficient in architecture design using AWS services
  • Experience in hands-on Engineering (Production level Python) - Not just scripting!
  • Experience in data science and financial data visualisation applications in Typescript
  • Excellent communication skills, with the ability to communicate fluently with both technical and non-technical audiences
  • Experience with data science tools, e.g. one or more of Spark, pandas, DuckDB, DataBricks, Snowflake
  • Knowledge of agile development and continuous delivery methodologies


Contact Ciara Clarke at Harrington Starr for a confidential discussion on this role.

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.

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.

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.

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.