Quantitative Research (Finance and ML) Engineer

  • Columbia, SC
  • Posted 23 hours ago | Updated 21 hours ago

Overview

On Site
Depends on Experience
Accepts corp to corp applications
Contract - W2
Contract - Independent
Contract - 12 Month(s)

Skills

ML
Quantitative Research
Finance

Job Details

7+ years in quantitative finance and AIML engineering.
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.

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About ZiksaTech LLC