Senior Data Scientist at Karkidi

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 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 or Brand team, you'll tackle a diverse set of challenges, such as optimizing freight costs, calculating optimal credit limits, personalizing landing pages for new retailers, predicting brand and retailer lifetime value, and improving product listings using AI. 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.

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.
• Underwriting: Improve Faire’s Net Terms portfolio by evaluating the creditworthiness of retailers on the platform. Use predictive modeling to dynamically assign credit limits that minimize default risk while maximizing growth. Leverage ML models to improve the retailer Identity Verification (IDV) experience, reducing default losses for new retailers.
• Retailer Growth: 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.
• Brand Growth: Prioritize brand leads for sales by predicting their lifetime value. Estimate the incremental value of new brands based on Faire’s existing selection and brand characteristics. Optimize how new brands are featured and explored.
• Listing Quality and Catalog Growth: Use AI techniques to detect and correct image issues, generate product titles and descriptions, extract product attributes, and predict product taxonomy. Perform entity resolution in order to match products across multiple data sources and link product variants.
• Marketplace Quality: Summarize and tag retailer reviews using LLMs. Detect and remove products that violate Faire’s policies, such as counterfeits. Use marketplace levers to direct retailers toward brands with higher service quality.

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

Canada: the pay range for this role is $156,000 to $214,500 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.

This role will be in-office on a hybrid schedule - Faire employees will be expected to go into the office 2 days per week on Tuesdays and Thursdays, effective the week of January 13, 2025. Additionally, in-office roles will have the flexibility to work remotely up to 4 weeks per year.

Applications for this position will be accepted for a minimum of 30 days from the posting date.