Senior Applied AI/ML Scientist - Fulfillment

Faire•San Francisco, CA

12h•Hybrid

About The Position

Faire leverages the power of machine learning (ML) and data insights to revolutionize the wholesale industry, enabling local retailers to compete against giants like Amazon and big box stores. Our highly skilled team of Applied AI/ML Scientist and machine learning engineers specialize in developing algorithmic solutions for notification and recommender systems, advertising attribution, and Lifetime Value (LTV) predictions. Our ultimate goal is to empower local retail businesses with the tools they need to succeed. At Faire, the Data team is responsible for creating and maintaining a diverse range of algorithms and models that power our marketplace. We are dedicated to building machine learning models that help our customers thrive. As an Applied AI/ML Scientist on the Retailer team, you'll tackle a diverse set of challenges, such as optimizing logistics and freight costs and calculating optimal credit limits. You'll also contribute to growing Faire’s retailer base by enhancing Search Engine Optimization, personalizing landing pages for new retailers, and predicting retailer lifetime value. You'll collaborate closely with other Applied AI/ML Scientist, engineers, and product managers to drive projects that unlock value from our unique, rich, and rapidly growing two-sided marketplace data. Our team already includes experienced Applied AI/ML Scientist and Machine Learning Engineers from Uber, Airbnb, Square, Facebook, and Pinterest. Faire will soon be known as a top destination for Applied AI/ML Scientist and machine learning, and you will help take us there!

Requirements

An advanced degree (MS or PhD) in a relevant discipline such as statistics, economics, econometrics, mathematics, computer science, operations research, etc.
Strong machine learning skills and 3+ years of experience productionizing machine learning models (Sklearn, XGBoost, or Deep Learning)
Strong programming skills (Python, Java, Kotlin, C++)
Knowledge of statistical techniques such as experimentation and causal inference
An excitement and willingness to learn new tools and techniques

Nice To Haves

SQL or other database querying experience preferred

Responsibilities

Build ML models that provide accurate shipping cost estimates.
Engineer new features to improve model performance.
Build ML models to forecast demand for the SKUs we should stock in our warehouses.
Apply predictive models to optimize shipping logistics—improving reliability while reducing costs.