Quantitative Research Analyst (Data Modeling & Imputation)

Location: Chicago, IL or Boston, MA (Hybrid)

Duration: 6 Months

Key Responsibilities

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.

Required Skills

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.

Top Resume Keywords

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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Quantitative Research Analyst