Quantitative Research Engineer//Charlotte, NC //Contract

Position: Quantitative Research Engineer

Required old LinkedIn profile only

Location: Charlotte, NC (Hybrid)

Employment Type: Contract

• 9+ years in quantitative finance, ML engineering, or similar.
• Build the price engine. Design, train, and deploy time-series and tree-based models (XGBoost, CatBoost, sklearn, lightGBM) that predict fair value and forecast volatility.
• Harden the data layer. Ingest and reconcile auction feeds, marketplace listings, and private-sale data. Handle splits, dupes, zero-comp situations, and stale marks.
• Ship to production. Own model orchestration with Airflow, feature stores, real-time inference endpoints, and rollback strategies.
• Quantitative R&D. Test market microstructure effects (extended bidding, buyer premiums, cash advances) and bake insights into pricing logic.
• API & analytics. Expose Alt Value as a public API, power in-app price alerts, and deliver dashboards the business can act on.
• Python, SQL, AWS (S3, ECS, Lambda), Airflow, dbt, Postgres, Spark, XGBoost, sklearn, CatBoost, GitHub Actions. (Nice-to-have: TypeScript, FastAPI, Grafana, Datadog)
• Deep time-series and forecasting experience, ideally on illiquid or auction assets.
• Proven path from Jupyter to production with CI/CD, testing, and automated monitoring.
• Track record of improving MAE or PnL with your models in live systems.
• Fluent in Python, SQL, and modern data tooling.
• Strong communication: you can explain heteroscedastic noise to engineers.

Look forward to hearing from you.

Thanks & Warm Regards,