Senior Applied AI/ML Scientist - Fulfillment

Rozgoo; San Francisco (CA); Hybrid; USD 196,000 - 270,000; Full time

Benefits offered by this job

Equity

Benefits

Hybrid work model

Job summary

Faire leverages ML and data insights to revolutionize the wholesale industry, empowering local retailers to compete against giants like Amazon. As an Applied AI/ML Scientist on the Retailer team, you’ll tackle challenges from forecasting demand to optimizing shipping costs and predicting retailer lifetime value.

You’ll collaborate with other ML scientists, engineers, and product managers to unlock value from rich marketplace data.

Qualifications

• Advanced degree (MS or PhD) in statistics, economics, econometrics, mathematics, CS or OR.
• 3+ years of experience deploying ML models (Sklearn, XGBoost, or DL).
• Strong programming skills in Python, Java, Kotlin, or C++.
• Knowledge of experiments and causal inference.
• SQL or similar querying experience preferred.
• Eagerness to learn new tools and techniques.

Responsibilities

• Shipping cost optimization: build ML models for cost estimates and features to improve performance.
• Fulfillment: forecast demand and optimize shipping logistics to reduce costs and improve reliability.

Skills

ML models

Python

Java

C++

Statistics

SQL

Learning agility

Education

MS/PhD in a quantitative field

Tools

Sklearn

XGBoost

Deep Learning

Job description

About Faire

Faire is a technology wholesale platform built on the belief that the future is local. Independent retailers around the globe collectively represent a multi-hundred-billion-dollar wholesale market that has historically been fragmented and offline. At Faire, we're using the power of tech, data, and machine learning to connect this thriving community of entrepreneurs across the globe. Picture your favorite boutique in town — we help them discover the best products from around the world to sell in their stores. With the right tools and insights, we believe that we can level the playing field so businesses can grow and local communities can thrive.

We’re looking for smart, resourceful and passionate people to join us as we power the shop local movement. If you believe in community, come join ours.

About this role

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!

What you’ll do

• 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.
• Fulfillment: Fulfillment has the potential to improve wholesale buying for both retailers and brands by an order of magnitude. 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.

Qualifications

• 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

Salary Range

San Francisco: the pay range for this role is $196,000 to $269,500 per year.

This role will also be eligible for equity and benefits. Actual base pay will be determined based on permissible factors such as transferable skills, work experience, market demands, and primary work location. The base pay range provided is subject to change and may be modified in the future.

Hybrid Faire employees currently go into the office 3 days per week on