Overview
Skills
Job Details
Job Title: Quantitative Research Engineer
Location: Charlotte, NC (Hybrid)
About the Role:
We are seeking a highly skilled Quantitative Research Engineer with deep expertise in machine learning, time-series modeling, and financial data engineering. This role focuses on building robust pricing engines, forecasting models, and production-grade data pipelines that drive intelligent pricing decisions in auction-based marketplaces.
Key Responsibilities:
Design, train, and deploy models (XGBoost, CatBoost, LightGBM, sklearn) for price prediction and volatility forecasting
Build and maintain a reliable data layer: ingest and clean auction, listing, and private-sale data
Handle data irregularities: splits, duplicates, stale or zero-comp data
Orchestrate and monitor models using Airflow, feature stores, and CI/CD pipelines
Conduct quantitative R&D to test microstructure effects (e.g., extended bidding, buyer premiums)
Develop and expose pricing models via public APIs and internal dashboards
Collaborate with engineering and product teams to ship models into production
Required Skills:
9+ years in quantitative finance, ML engineering, or related field
Proven experience in time-series forecasting, especially with illiquid or auction-style assets
Strong skills in Python, SQL, and ML libraries (XGBoost, CatBoost, LightGBM, sklearn)
Experience with AWS (S3, ECS, Lambda), Airflow, dbt, Postgres, Spark
CI/CD experience with tools like GitHub Actions; solid testing and monitoring practices
Ability to explain complex quantitative concepts clearly to technical and non-technical stakeholders
Nice-to-Have Skills:
TypeScript, FastAPI, Grafana, Datadog
Familiarity with market microstructure or auction-based pricing models
Demonstrated improvements in live systems (MAE, PnL, model drift, etc.)