Senior Data Scientist

Right now is one of the most exciting moments to join Popsa. Deloitte named us one of the UK’s fastest-growing technology companies, and the Financial Times recognised Popsa among the Top 5 fastest-growing software companies in Europe. We’re backed by world-class investors, and the world is noticing. Our iOS and Android apps are available in 12 languages, trusted by more than 12 million people who have created over 5 million photobooks shipped to 50+ countries. With more than 350,000 five-star reviews, we’re one of the highest-rated consumer apps anywhere. But the real story isn’t the numbers, it’s the memories behind them. We’re living in a time where we capture more photos than ever before. Billions of moments sitting unseen in camera rolls and clouds, mixed with screenshots, receipts, or where we parked the car. We are great at documenting life… but not so great at preserving the meaning in it. Popsa is changing that. Since launching in 2016, we’ve built an award-winning platform that removes every barrier to transforming your favourite memories into something beautifully tangible. No design skills. No time-sink. Just the joy of holding real stories in your hands. A celebration of the people and moments that make life worth living. This is only the beginning. 👉 Learn more about what it’s like to work at Popsa 👉 Read about the technology we're building 👉 Take a look inside our Soho HQ About the Role You'll design, train, and deploy machine learning systems that directly impact millions of users; from computer vision models that understand photo libraries at scale, to generative AI features that write captions, suggest titles, and curate personalised "Memories" albums. You'll own the full lifecycle from prototyping to production, collaborating closely with engineering and product teams to ship features that help people rediscover the moments that matter. Exciting projects you'll work on

Develop large-scale personalised curation systems that process entire photo libraries, applying computer vision and machine learning to detect events, relationships, and themes, and automatically generate structured "Memories" albums at scale

Apply generative AI across the company; from real customer-facing product features such as title suggestions and captions to internal systems such as customer support

Build and deploy large-scale convolutional neural networks trained on tens of millions of images, with datasets growing daily

Scale inference and serving infrastructure, our FastAPI servers handle millions of requests in production

Expertise

Excellent coding ability in Python

Solid experience with SQL for querying and transforming large datasets

Strong understanding of statistics, data mining, and predictive analytics techniques

Experience developing and applying various machine learning algorithms, including classification, regression, and clustering

Hands-on experience deploying and testing generative AI models in production

Comfort with engineering-heavy workflows (CI/CD, containers, infrastructure)

Hands-on experience with Docker and Terraform

Exposure to AWS (e.g. S3, ECS, SageMaker, EC2)

Analysis of user data to personalise content, improve engagement, and optimise user journeys

Mindset

A problem-solving, curious, and inquisitive attitude

Clear communicator with a keenness to work collaboratively, able to convey complex findings to both technical and non-technical stakeholders

Constructive approach to code reviews and architectural discussions

Responsibilities

Design, train, and deploy machine learning models that directly impact customers every day

Own the full stack of data science development: from prototyping models to production deployment and monitoring

Clean, transform, and prepare data for analysis, handling missing values and inconsistencies to ensure reliability

Extract insights from large, complex datasets (structured and unstructured) to identify trends, build predictive models, and develop algorithms for creative applications

Collaborate closely with product, engineering, and marketing teams to ship features end-to-end

Contribute to improving infrastructure for large-scale training, testing, and evaluation

Design and conduct statistical experiments to evaluate creative strategies and optimise outcomes

Present data findings and insights in a meaningful and visually compelling way to diverse audiences, including creative teams and leadership

Stay updated on the latest advancements in data science, share knowledge, and contribute to team learning

Nice to Have

Experience with recommender systems

Familiarity with large-scale training pipelines (distributed training, GPU scaling)

Experience with multi-head CNN architectures or multi-task learning for robust model training

Contributions to open source, technical talks, or side projects showing initiative