Data Scientist (Advanced ML experience)

algo1
London
2 weeks ago
Applications closed

Related Jobs

View all jobs

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist (eDV clearance required)

Data Scientist (Advanced Machine Learning Applications)

Algo1: London Area, United Kingdom

Hybrid: Minimum 3 days per week in Central London office


About the Role:

We are a VC-backed startup focused on behavioural AI, currently in stealth mode. We are developing innovative solutions for retail, leveraging reinforcement learning, generative AI, and advanced machine learning. We are seeking a Data Scientist with recent, hands-on experience in developing and deploying advanced machine learning models for complex datasets to work with our team of industry-leading domain experts and engineers.


Key Responsibilities:

  • Algorithm Development & Implementation:Design and implement scalable machine learning and deep learning algorithms for complex data analysis and predictive modeling.
  • Solution Development:Translate the latest advances in machine learning into impactful solutions and products, from rapid MVPs to fully deployed, production-ready systems.
  • Performance Optimisation:Optimise machine learning models for peak performance in modern environments.


Essential Qualifications:

  • Education:Master's degree (MSc) in Computer Science, Machine Learning, or a related field.
  • Practical Experience:Proven experience implementing advanced machine learning solutions in a commercial setting.
  • Machine Learning Expertise:Strong foundational knowledge of machine learning and deep learning algorithms, including deep neural networks, supervised/unsupervised learning, predictive analysis, and forecasting.
  • Programming Skills:Expert-level proficiency in Python, with a demonstrated ability to develop and debug production-grade code.


Desired Skills (Bonus Points):

  • Deep Learning Frameworks:Advanced expertise with PyTorch and Hugging Face Transformers.
  • Retail Analytics:Strong practical understanding of retail analytics including consumer segmentations, personalisation systems, campaign effectiveness and media measurement.



What We Offer:

  • The opportunity to be a founding member of a groundbreaking startup.
  • A dynamic and collaborative work environment.
  • A chance to work with cutting-edge technologies and solve challenging problems.
  • Competitive compensation.
  • Equity in a rapidly growing company.


If you're an energetic data scientist who thrives in a fast-paced environment and wants to make a real impact on the future of retail, we encourage you to apply.

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.