Role overview
- Have a solid foundation in traditional ML techniques and the model lifecycle, with the practical expertise to handle class imbalance, tune hyperparameters, and resolve common pitfalls like overfitting.
- Have demonstrable experience designing, deploying, and monitoring ML services to solve customer and business problems.
- Have strong programming skills in Python for delivering production-ready, well-structured and documented code.
- Have experience with large datasets and are proficient with SQL, exposure to Snowflake and dbt is a plus.
- Are curious about customer needs and take a pragmatic, data-driven, and experimental approach to solving problems.
- Thrive in collaborative environments and work effectively with a range of people and teams.
- Bring a positive, optimistic mindset, overcoming setbacks and motivating those around you.
- Are keen to learn and stay up-to-date with the latest technologies and value sharing your knowledge with your peers.
- It would be great if you also have...
- experience working on an e-commerce site or in a fast-growing (preferably consumer-facing) start-up.
- experience working in a fully-remote setting.
- These are some of the skills and experience we think will enable success in this role, but please don’t worry if you are missing some of these. We’re committed to building a team made up of different strengths, skills and experiences, so if you’re excited about our values, passionate about what you do and would like to join us in doing things more thoughtfully, we’d love to hear from you.
What you'll work on
- Have a critical role in architecting, implementing, and maintaining production-grade, low-latency ML services for ranking models, recommendation algorithms, and forecasting methods.
- Collaborate with data scientists, product managers and other teams to brainstorm best approaches for solving the problems at hand, be they product-related or with our infrastructure.
- Help design experimentations to test our ideas and assess improvements to our models.
- Advise on data strategy to provide datasets for future data science projects.
- Deliver ML models with agreed engineering standards to ensure that our capabilities are resilient, scalable and future-proof.
- Enhance our AWS-native MLOps platform, and guarantee high availability and low-latency inference for our models.
- Bring energy and positivity to the role, looking for every opportunity to learn and craft the role around our values: care wildly; think deeply, act swiftly; stay open, be curious; lead change for good.
Tags & focus areas
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