▷ 3 Days Left! Head of Data Science & Applied AI NewRemote, UK

Prolific
uk
1 day ago
Create job alert

Prolific is not just another player in the AI space –we are the architects of the human data infrastructure that'sreshaping the landscape of AI development. In a world wherefoundational AI technologies are increasingly commoditised, it'sthe quality and diversity of human-generated data that trulydifferentiates products and models. The Role Lead the technicalteam that's revolutionising how AI learns from humans. At Prolific,you'll build and direct the data science and AI/ML engineeringfunction that powers the world's leading platform for humanfeedback collection – enabling AI developers to efficientlyincorporate human intelligence into their models. Your team willsolve challenges across the full ML/AI spectrum: creatingintelligent systems that optimize feedback quality, designbehavioural modeling at scale, and developing breakthrough methodsfor effective human-AI interaction. This isn't just anothertechnical leadership role; you'll directly shape how the industryincorporates human intelligence into the next generation of AIsystems, with immediate access to a unique human data asset thatpositions you to make outsized impact. What You’ll Be DoingStrategic Leadership - Develop and execute Prolific's data scienceand applied AI strategy - Build and scale high-performing teams ofdata scientists and AI/ML engineers, with a strong culture ofexcellence, innovation, and impact - Partner directly withexecutive leadership to identify breakthrough opportunities whereour human data advance AI capabilities - Create a technical visionthat positions Prolific as the leader in human-centered AIdevelopment - Establish a technical culture that attracts andretains exceptional talent in a competitive market TechnicalDirection - Lead the development of sophisticated ML/AI systemsthat enhance how human feedback is collected, validated, andutilized - Spearhead the creation of robust measurement frameworksand experimental designs to quantify our platform's capabilitiesand support emerging AI evaluation needs - Establish technicalstandards and best practices across data science and AI engineering- Balance technical innovation with operational excellence andbusiness impact Cross-Functional Impact - Translate technicalcapabilities into competitive advantages for the platform -Collaborate with platform engineering to create seamlessintegration paths for your team's innovations - Partner withresearch to rapidly operationalize promising approaches - Work withproduct teams to enhance platform capabilities through intelligentsystems - Influence how the AI industry approaches human feedbackthrough thought leadership What You’ll Bring - 6+ years ofexperience in data science, AI/ML engineer, or related fields –preferably in leadership roles - Proven track record of buildingand scaling high-performing teams - Experience applying bothtraditional machine learning and modern AI techniques to solve realbusiness problems - Strategic vision for how human data canfundamentally improve AI systems - Experience working withbehavioral data, human feedback systems, or AI evaluationmethodologies preferred – with interest in exploring innovativeapplications - Ability to work quickly in a fast paced challengingenvironment and deliver high quality results to stakeholders -Experience collaborating effectively with platform engineering andresearch teams - Demonstrated ability to balance innovation withpractical delivery of robust, scalable systems - Exceptionalcommunication skills with ability to influence at all levels of theorganization - Strategic mindset with hands-on technicalcapabilities and a practical approach to problem-solving WhyProlific is a great place to work We've built a unique platformthat connects researchers and companies with a global pool ofparticipants, enabling the collection of high-quality, ethicallysourced human behavioural data and feedback. This data is thecornerstone of developing more accurate, nuanced, and aligned AIsystems. We believe that the next leap in AI capabilities won'tcome solely from scaling existing models, but from integratingdiverse human perspectives and behaviours into AI development. Byproviding this crucial human data infrastructure, Prolific ispositioning itself at the forefront of the next wave of AIinnovation – one that reflects the breath and the best of humanity.Working for us will place you at the forefront of AI innovation,providing access to our unique human data platform andopportunities for groundbreaking research. Join us to enjoy acompetitive salary, benefits, and remote working within ourimpactful, mission-driven culture. #J-18808-Ljbffr

Related Jobs

View all jobs

▷ 3 Days Left! Machine Learning Engineer (UK)

▷ (3 Days Left) Senior Data Scientist - CoreProducts

▷ Apply in 3 Minutes: Machine Learning Engineer, EnterpriseResearch London, UK

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.

Portfolio Projects That Get You Hired for Machine Learning Jobs (With Real GitHub Examples)

In today’s data-driven landscape, the field of machine learning (ML) is one of the most sought-after career paths. From startups to multinational enterprises, organisations are on the lookout for professionals who can develop and deploy ML models that drive impactful decisions. Whether you’re an aspiring data scientist, a seasoned researcher, or a machine learning engineer, one element can truly make your CV shine: a compelling portfolio. While your CV and cover letter detail your educational background and professional experiences, a portfolio reveals your practical know-how. The code you share, the projects you build, and your problem-solving process all help prospective employers ascertain if you’re the right fit for their team. But what kinds of portfolio projects stand out, and how can you showcase them effectively? This article provides the answers. We’ll look at: Why a machine learning portfolio is critical for impressing recruiters. How to select appropriate ML projects for your target roles. Inspirational GitHub examples that exemplify strong project structure and presentation. Tangible project ideas you can start immediately, from predictive modelling to computer vision. Best practices for showcasing your work on GitHub, personal websites, and beyond. Finally, we’ll share how you can leverage these projects to unlock opportunities—plus a handy link to upload your CV on Machine Learning Jobs when you’re ready to apply. Get ready to build a portfolio that underscores your skill set and positions you for the ML role you’ve been dreaming of!

Machine Learning Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

Machine learning is fuelling innovation across every industry, from healthcare to retail to financial services. As organisations look to harness large datasets and predictive algorithms to gain competitive advantages, the demand for skilled ML professionals continues to soar. Whether you’re aiming for a machine learning engineer role or a research scientist position, strong interview performance can open doors to dynamic projects and fulfilling careers. However, machine learning interviews differ from standard software engineering ones. Beyond coding proficiency, you’ll be tested on algorithms, mathematics, data manipulation, and applied problem-solving skills. Employers also expect you to discuss how to deploy models in production and maintain them effectively—touching on MLOps or advanced system design for scaling model inferences. In this guide, we’ve compiled 30 real coding & system‑design questions you might face in a machine learning job interview. From linear regression to distributed training strategies, these questions aim to test your depth of knowledge and practical know‑how. And if you’re ready to find your next ML opportunity in the UK, head to www.machinelearningjobs.co.uk—a prime location for the latest machine learning vacancies. Let’s dive in and gear up for success in your forthcoming interviews.

Negotiating Your Machine Learning Job Offer: Equity, Bonuses & Perks Explained

How to Secure a Compensation Package That Matches Your Technical Mastery and Strategic Influence in the UK’s ML Landscape Machine learning (ML) has rapidly shifted from an emerging discipline to a mission-critical function in modern enterprises. From optimising e-commerce recommendations to powering autonomous vehicles and driving innovation in healthcare, ML experts hold the keys to transformative outcomes. As a mid‑senior professional in this field, you’re not only crafting sophisticated algorithms; you’re often guiding strategic decisions about data pipelines, model deployment, and product direction. With such a powerful impact on business results, companies across the UK are going beyond standard salary structures to attract top ML talent. Negotiating a compensation package that truly reflects your value means looking beyond the numbers on your monthly payslip. In addition to a competitive base salary, you could be securing equity, performance-based bonuses, and perks that support your ongoing research, development, and growth. However, many mid‑senior ML professionals leave these additional benefits on the table—either because they’re unsure how to negotiate them or they simply underestimate their long-term worth. This guide explores every critical aspect of negotiating a machine learning job offer. Whether you’re joining an AI-focused start-up or a major tech player expanding its ML capabilities, understanding equity structures, bonus schemes, and strategic perks will help you lock in a package that matches your technical expertise and strategic influence. Let’s dive in.