Unlocking the value of alternative assets, starting with the $5B trading-card market, by developing a real-time pricing model at scale
Requirements
- 10+ years of total engineering experience with at least 4-5 years of direct machine learning experience
- Expertise in Python with hands-on experience using libraries such as scikit-learn, XGBoost, and pandas
- A strong foundation in ML Ops and infrastructure, with experience deploying models on AWS using tools like ECS and Docker
- Experience in data orchestration using Airflow for model training and batch jobs
- Experience working with Random Forest, ensemble methods or pricing/underwriting models in a similar marketplace environment
Responsibilities
- Optimize our pricing model to significantly reduce infrastructure costs while maintaining and improving its accuracy especially for high value cards
- Iterate on our underwriting model to maximize cash advance disbursements while maintaining target risk thresholds and default rates
- Lead the full ML lifecycle from model training and feature generation to production deployment and monitoring
- Design and execute experiments and backtesting to discover and validate new features that improve the model's predictive power
- Own the model's AWS infrastructure, writing code for our pricing API to ensure the model can serve at scale and with low latency
- Collaborate closely with our Expert Pricers to become a domain expert in the trading card market and inform model improvements
Other
- Passionate about trading cards or a similar alternative asset class, with a desire to go deep on the domain
- Hands-on individual contributor who thrives in a zero-to-one startup environment
- Want to own a business-critical system and have the opportunity to build a team around you
- Pragmatic and prefer to build a solution to a problem, not replace an entire system just for the sake of it
- Highly curious with a strong desire to learn
