research
Posted Apr 27Senior Applied AI/ML Scientist - Fulfillment
at Faire
San Francisco, United StatesHybrid
Responsibilities
- Engineer new features to improve model performance.
Requirements
- At Faire, we're using the power of tech, data, and machine learning to connect this thriving community of entrepreneurs across the globe.
- 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.
- 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 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!
- Shipping cost optimization: Build ML models that provide accurate shipping cost estimates.
- This role involves building ML models to forecast demand for the SKUs we should stock in our warehouses, and applying predictive models to optimize shipping logistics—improving reliability while reducing costs.
- An advanced degree (MS or PhD) in a relevant discipline such as statistics, economics, econometrics, mathematics, computer science, operations research, etc.
- 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
- SQL or other database querying experience preferred
- Equipped to scale: We invest in what matters, including the latest enterprise AI tools, to help you work smarter and get more out of every day.