Data Science Manager (Applied AI)

Trustpilot
Edinburgh
2 months ago
Applications closed

Related Jobs

View all jobs

Data Science Manager – Property Tech – London

Data Science Manager - Property Tech - London

Data Science Manager – Property Tech – London

Manager, Data Science

Data Science and Analytics Manager

Data Analytics Manager

Data Science Manager – Applied AI

At Trustpilot, we’re on an incredible journey. We’re a profitable, high‑growth FTSE‑250 company with a big vision: to become the universal symbol of trust. We run the world’s largest independent consumer review platform and are looking for a Data Science Manager to join our Applied AI team. The role will help us transform data into value, drive product innovation, improve user experience, enrich Trustpilot’s data ecosystem and enhance business growth.


What You’ll Be Doing

  • Line‑manage a small team of Data Scientists.
  • Lead the delivery of multiple Applied AI projects, balancing delivery with best practices and standardisation.
  • Collaborate closely with product management, UX, data analytics and engineering across multiple product contexts.
  • Translate product and business requirements into Applied‑AI deliverables, and occasionally contribute to hands‑on delivery.
  • Prioritise and shape Applied‑AI deliverables across product innovation, maintenance, research and foundational efforts, in line with product roadmaps and the Applied AI Center of Excellence.
  • Use NLP, topic modelling, recommendation, classification and generative AI to identify opportunities to improve existing AI models.
  • Collaborate with the MLOps team to standardise deployment practices, minimise manual effort and improve maintenance efficiency.
  • Promote a culture of collaboration, accountability, technical excellence, innovation and high performance.
  • Attract, engage and retain Applied AI scientists, supporting their growth.
  • Ensure teams maintain high technical proficiency and quality.
  • Manage high‑risk elements of major initiatives and ensure alignment and transparency with stakeholders.
  • Enable other functions to perform at their best through effective, enduring partnerships.

Who You Are

  • Extensive experience as a Data Scientist or Applied AI Scientist, with a proven track record in leading teams.
  • Ability to use data and metrics to inform decisions, effect change and align Applied‑AI efforts with business/product goals.
  • Strong stakeholder‑management experience, creating alignment across teams and working with the wider business.
  • Excellent communication skills for both technical and non‑technical audiences.
  • Aspired to contribute to Trustpilot’s future, making pragmatic technical decisions.
  • Advanced statistical, machine‑learning and generative‑AI expertise for online content analysis (sentiment, sequence analysis, forecasting).
  • Experience building and deploying reproducible, production‑ready AI/ML models at scale, coupled with solid data‑engineering skills.
  • Prior knowledge of NLP and generative AI is essential.
  • Experience with large‑scale datasets from tech platforms, e‑commerce or SaaS products, and behavioural data for intelligent product features.
  • Proficiency in Python, R and SQL for data manipulation, modelling and scripting.
  • Experience with cloud services (AWS or Google Cloud) for scalable AI/ML development and deployment, and knowledge of data pipelining tools such as Airflow.
  • Bachelor’s degree in Statistics, Mathematics, Physics, Computer Science or a related quantitative field; Master’s or PhD preferred.

Benefits

  • Flexible working options.
  • Competitive compensation package and bonus.
  • 25 days holiday per year, increasing to 28 after 2 years.
  • Two paid volunteering days per year.
  • Learning and development support via Trustpilot Academy and Blinkist.
  • Employer pension and life insurance.
  • Health cash plan, online GP, 24/7 Employee Assistance Plan.
  • Full access to Headspace mindfulness app.
  • Paid parental leave.
  • Season ticket loan and cycle‑to‑work scheme.
  • Central office with snacks and refreshments.
  • Regular social events, company celebrations and ERG activities.
  • Access to over 4,000 deals and discounts.

About Us

Trustpilot began in 2007 with a simple yet powerful idea: to be the universal symbol of trust, bringing consumers and businesses together through reviews. Today we host more than 300 million reviews and 64 million monthly active users, with 140 billion annual Trustbox impressions. We’re headquartered in Copenhagen with global operations in Amsterdam, Denver, Edinburgh, Hamburg, London, Melbourne, Milan and New York. Our culture is built on collaboration, respect, and diversity.


Equal Opportunity Statement

Trustpilot is committed to creating an inclusive environment where people from all backgrounds can thrive, and where different viewpoints and experiences are valued and respected. Trustpilot will consider all applications for employment without regard to race, ethnicity, national origin, religious beliefs, gender identity or expression, sexual orientation, neurodiversity, disability, age, parental or veteran status. We value the heart of trust in our diverse community.


Location: Edinburgh, Scotland, United Kingdom


Employment type: Full‑time


Seniority level: Not applicable


Job function: Engineering and Information Technology; Industries: Technology, Information and Internet


#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.