jobber - Staff Data Scientist, Applied ML

• Production ML experience, end-to-end. You've trained, deployed, served, and maintained models that acted on real users (not just scored offline) You've learned the hard lessons about retraining, drift, and technical debt that come with it. • A strong statistics foundation. You can reason about bias and variance, justify a loss function, and tell signal from noise without hand-waving. • Expert SQL and production-grade Python skills. • Depth in modern ML methods: Deep learning and neural architectures, transformers/BERT-family models, RNN/CNN, ranking, and representation learning, with hands-on experience applying LLMs in production systems, including RAG and context management. • Experience designing for the real cost of being wrong. You've built or tuned custom and asymmetric loss functions where a miss in one direction is far more expensive than the other, and you can explain the business reasoning behind the choice. • Experience with large data in production environments and the platforms that support it. ML/AI platforms such as Snowflake, orchestration with Apache Airflow, and cloud infrastructure (AWS strongly preferred; GCP or Azure equivalent considered). • Strong communication and stakeholder alignment skills. This team's work only lands if everyone shares the same understanding of what the data means. You can take a room of technical and non-technical partners from confusion to alignment, present uncertainty honestly, and influence without authority. • Ownership of quality over shipping speed alone. You want to know why something works and how to make it better. You don't accept a model's current performance as its ceiling, and you don't outsource your understanding of your own code to an AI assistant. • It would be nice if you had: • Graph experience: Graph theory, graph neural networks, knowledge graphs, or graph databases such as Neo4j. Rare, and the single most valuable thing you could bring to this role. • A software engineering background: Services and model serving (REST/gRPC), Docker/Kubernetes, CI/CD, feature stores. • Experience with Snowflake, including Snowpark and Snowpark Container Services, or a comparable path from warehouse to deployed model. • Experience building LLM evaluation infrastructure, safety layers, or LLMOps for customer-facing AI. • Exposure to risk, fraud, or fintech modelling, or to recommendation and ranking systems at scale.