About Hey Savi
We’re a fully female-founded company on a mission to change the way people search and shop online for fashion…forever! We’re going to spark a new era of fashion discovery, igniting confidence in everybody and every body, and we’ll create a world where fashion confidence starts with “Hey Savi…”.
Hey Savi is at the beginning of an exciting journey and we’re looking for top talent to join our team. Because data, and specifically our data science models, are our IP so you’ll have a major role in shaping the product and experience. Unlike many start-ups we’re very well funded, have a detailed business and financial plan, and are looking for experienced, passionate professionals to join us in creating and scaling a game-changing business.
So if you want a role where you will make a major impact and want to be a part of a team of women building an incredible product and experience for other women, come join us and make the most Savi move of your career!
About the Role
We’re looking for a Data Scientist to join our team and work with our Head of Data Science in building the engine that drives our product and experience. The power of data, including Machine Learning and AI, and how to use it effectively and ethically, will shape everything we do now and as we grow, so this is a pivotal role. We’re looking for someone with top-notch abilities in handling various data types and who has expertise in delivering projects across multiple disciplines, including experience in either computer vision or Recommendations & Personalisation, and a strong interest and expertise in LLMs.
You’ll be working closely with the full product team including, a top-notch Researcher, stellar Product Designer, highly experienced Product Manager, and visionary Head of Engineering, as well as a world-class Head of Data Science, which this role reports to.
Requirements
Responsibilities
- Role: Data Scientist
- Experience: Approx 3-4 years
Must Haves:
- Data: Great understanding of data and common analysis, enhancement and transformation techniques to understand limitations and validity of a dataset
- Exposure to variety of data types and volumes
- Understanding of data collection and annotation
- NLP & LLMs: good understanding and experience in handling text data
- Strong understanding of foundational techniques and models in NLP
- Experience in working with small and large language models
- Understanding of underlying concepts behind the models and their limitations
- Understanding and experience in techniques to improve performance of LLMs
- RAG, few-shot learning, multi-agents
- Fine-tuning models
- Understanding and experience in evaluating performance of large language models
Nice to Haves
- Computer Vision: Good understanding and experience in handling image data
- Strong understanding of predictive techniques including neural network and transformer architectures
- Understanding of model applications and limitations
- Good grasp of underlying concepts and maths behind the ML models
- Highly Valued: ability/experience in customising existing architectures to adapt to a specific use case/overcome model limitations
- Experience with training and fine-tuning pre-trained models
- Experience and strong understanding of evaluation techniques in object detection and recommendations
- Recommendations & Personalisation: good understanding and experience in handling customer and product level data
- Experience in implementing ML and stats based models in supervised and unsupervised learning context
- Deep understanding of underlying mathematics behind the models
- Experience in evaluating models performance
Additional Skills
- Programming skills:
- Language: Python
- Frameworks and libraries:
- Core python libraries like pandas, numpy, opencv, scikit-learn etc.
- Pytorch, Keras, Tensorflow
- Huggingface, Langchain, Langgraphs
- Streamlit (or other alternatives)
- Cloud Technologies:
- AWS
- Knowledge of appropriate services within AWS (such as Lambda, EC2, S3, RDS, DynamoDB and etc)
- Understanding on how they can work together in a single pipeline
- Experience with building pipelines within AWS for data science projects
- Serving ML endpoints
- Strong understanding of computational resources and their differences
- Understanding of concepts like cost and computational efficiency
- Other: GitHub
- Development and deployment:
- Good knowledge of best practices in MLOps
- Production-level experience: hands-on experience working with ML models in production environments
- Experience with following engineering best practices of deploying the models and machine learning pipelines
- Experience in working collaboratively with engineers
- Understanding and experience in post-deployment techniques
- Knowledge of concepts such as data and model drift
- Knowledge of concepts such as feedback loop
- Education:
- Bachelor’s degree in relevant fields such as mathematics, data science, computer science or statistics
- NOTE: If you have the experience and expertise for the role but don’t have a degree we strongly encourage you to apply as what matters is your ability to do the job well, especially if you have the complex competencies listed below!
Complex Competencies
We know HOW you work is as important as what you work on so we’re looking demonstrable skills and experience with the following competencies and ways of working:
- Intellectual Curiosity: Interest in keeping up to date with new techniques and technologies in data science space
- Flexible Thinking: Good understanding of experimentation concepts and ability to pivot quickly
- Collaboration: Enjoy working in collaborative environment on a single project
- Agility: Familiarity with agile working environments and how to leverage them to increase quality and speed and decrease risk for delivery
- Independence: Ability to work independently on individual projects
- Communication Skills: Active listener who can adjust their communication style to ensure understanding and alignment across a diverse set of people
- Presentation Skills: Ability to deliver and present demos that can be easily digested by the wider non-technical audiences to help them understand the value provided and the goals achieved
- Organisation: Structured approach to documentation and project work
PLEASE NOTE: If you don’t meet 100% of the criteria but are passionate about our mission and vision and think you can do the job, especially if you have expertise with the complex competencies listed above, we strongly encourage you to apply!
Benefits
Location + Work Style
We’ll all be where we need to be based on what’s happening. We’ll have in-person team sessions (usually once a week) as needed for key activities like planning, strategy, and brainstorming sessions (and some fun!), and remote work the rest of the time to allow for flexibility, work-life balance, and quiet time for deep work. Savi is based in London and are looking for people in the UK and Europe to join our team. We regret that we can’t hire candidates from other locations or provide Visa sponsorship yet.
Salary Range:
UK Based: £50k-£80k GBP Annually
