What you'll do:

📍Build and help set direction across the entire machine learning development process to implement machine learning algorithms in production, including exploratory data analysis, data modeling, feature engineering, model training and tuning, testing, deployment, and monitoring. 📍Partner closely across the business to identify improvements and influence decisions using data science methodologies and tools. 📍Develop new production machine learning algorithms and systems that enrich the app experience with machine learning-powered experiences. 📍Contribute across the data science and machine learning development stack: idea development, opportunity sizing, prototyping, testing, and deployment. 📍Design and implement end-to-end data pipelines and data systems that support MLOps and business processes. 📍Build high quality communication devices such as dashboards, notebooks, documents, presentations to convey insights across a broad audience. 📍Define and advance standard methodologies within an experiment-driven culture. 📍Bachelor’s degree in Computer Science, a related field, or equivalent work experience.

You

📍Curious about who thrives at Whatnot? We’ve found that low ego, a growth mindset, and leaning into action and high impact goes a long way here. 📍As our next Machine Learning Scientist you should have 5+ years of experience, plus: 📍Bachelor’s degree in Computer Science, Statistics, Mathematics, Software Engineering or related technical field., or equivalent work experience. 📍Industry experience with a track record of applying scientific methods to solve real-world problems on consumer scale data. 📍Experience leading work to develop and deploy machine learning- and data-based solutions in production. 📍Extensive experience with Python and SQL for data science, machine learning, and software development e.g. numpy, scipy, pandas, scikit-learn, PyTorch, LightGBM, Flask, FastAPI, Docker, Jupyter. 📍Ability to work autonomously and lead initiatives across multiple product areas and communicate findings with leadership and product teams. 📍Comfortability with data warehouses and transformation tools such as Snowflake, dbt, Dagster. 📍Proficiency and experience in applied statistics and machine learning fields e.g. Experimentation and 📍Causal Analysis, Recommendations, Fraud & Anomaly Detection, Natural Language, Computer Vision. 📍Firm grasp of visualization tools, interactive and self-serving, such as dashboards and notebooks. 📍Professionalism around collaborating in a remote working environment and well tested reproducible work. 📍Above average documentation and communication skills.