Quantitative Research Analyst (Data Modeling & Imputation)

Fusion

Chicago, IL, United States

2 months ago

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Role details

Contract type

Temporary to permanent

Employment type

Full-time (> 32 hours)

Experience level

Expert

Experience required

5 years minimum

Working hours

Regular working hours

Job source

www.careerjet.com

Tech stack

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

+5 more

Feature Engineering Apache Spark Pandas Data Pipelines Databricks

Job 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)

Requirements

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.