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# Quantitative Research Analyst (Data Modeling & Imputation)

- **Company:** Fusion
- **Location:** Chicago, IL, United States
- **Experience:** Expert
- **Contract:** Temporary to permanent
- **Skills:** Data Validation, Information Engineering, Extract Transform Load (ETL), Data Transformation, Data Normalization, Distributed Systems, Python (Programming Language), NumPy, Standard Sql, SQL Databases, Unstructured Data, Data Processing, Feature Engineering, Apache Spark, Pandas, Data Pipelines, Databricks
- **Published:** July 4, 2026
- **Apply:** https://www.careerjet.com/jobad/us1bf841ddd237f57a7623a056184757e1

## About the Role

* 2 5 years in Quantitative Research, Data Science, Financial Data Engineering, or similar.
 * Strong expertise in data wrangling, data transformation, ETL, and data engineering.
 * Hands-on experience with missing data imputation, time-series interpolation, cross-sectional inference, and model-based imputation.
 * Advanced Python (Pandas, NumPy) and SQL.
 * Strong knowledge of statistics, econometrics, data quality, bias detection, and validation.
 * Experience working with large, messy real-world datasets.
 * Understanding of equity markets and financial statements is preferred.

## Description

* Build scalable data pipelines for structured and unstructured data.
 * Develop missing data imputation frameworks using statistical and model-based techniques.
 * Perform data normalization, reconciliation, validation, and quality analysis.
 * Create model-ready datasets through data wrangling and feature engineering.
 * Write efficient, reusable Python (Pandas, NumPy) and SQL code.
 * Document methodologies and ensure reproducibility., * Data Wrangling
 * Data Transformation
 * ETL / Data Pipelines
 * Python
 * Pandas
 * NumPy
 * SQL
 * Missing Data Imputation
 * Time-Series Analysis
 * Cross-Sectional Analysis
 * Model-Based Imputation
 * Data Normalization
 * Data Reconciliation
 * Feature Engineering
 * Data Quality
 * Data Validation
 * Bias Detection
 * Statistics
 * Econometrics
 * Financial Data
 * Equity Markets
 * Financial Statements
 * Structured & Unstructured Data
 * Distributed Computing (Spark, Databricks) (Nice to Have)
 * NLP (Nice to Have)


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