onsite
Quantitative Research Analyst Data Modeling & Imputation - Value Technology Inc
Software Engineer
Design and implement imputation frameworks, data transformations, and modeling pipelines to turn fragmented financial data into reliable research-ready signals, using Python, SQL, and advanced statistical techniques.
About the role
Key Responsibilities
- Develop and maintain robust data imputation frameworks to handle missing, inconsistent, or fragmented financial data.
- Design and implement end‑to‑end data transformation and ETL pipelines that convert raw inputs into clean, analysis‑ready datasets.
- Build statistical and machine‑learning models that generate reliable signals for downstream research and analytics.
- Collaborate with data engineers and domain experts to ensure data quality, consistency, and reproducibility across pipelines.
- Document methodologies, code, and validation results to support transparent, research‑grade data products.
Requirements
- Strong proficiency in Python (pandas, NumPy, scikit‑learn) and SQL for data manipulation and analysis.
- Experience with statistical modeling, time‑series analysis, and data imputation techniques.
- Hands‑on experience building ETL pipelines and data transformation workflows.
- Solid understanding of financial data structures and the challenges of incomplete or noisy datasets.
- Excellent problem‑solving skills and ability to work independently in a fast‑paced, research‑focused environment.