Senior Applied AI/ML Scientist - Retailer

Faire•San Francisco, CA

6d•$211,000 - $290,500•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 data scientists 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 Science 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 a Data 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 data scientists, 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 Data Scientists and Machine Learning Engineers from Uber, Airbnb, Square, Facebook, and Pinterest. Faire will soon be known as a top destination for data scientists 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
SQL or other database querying experience preferred
An excitement and willingness to learn new tools and techniques

Responsibilities

Shipping cost optimization: Build ML models that provide accurate shipping cost estimates. Engineer new features to improve model performance. These models may use live carrier information and be both performant and explainable.
Underwriting: Improve Faire’s Net terms portfolio by evaluating creditworthiness of retailers on Faire’s platform. Use predictive modeling to dynamically assign credit terms limits that minimize default risk and maximize growth.
Retailer Growth & Lifecycle: Build models to automatically generate landing pages and content to target search engine demand. Use natural language processing to understand search engine keyword intent and match to relevant internal content. Build ML models to generate intelligence about retailers to power personalization. Predict retailer lifetime values to optimize retailer acquisition spend.