Quantitative Research Analyst (Data Modeling & Imputation)

Our client, a IT Services and Consulting company, is looking for a Quantitative Research Analyst (Data Modeling & Imputation)  for their Chicago, IL/Boston, MA/Hybrid location.
 
Responsibilities:

• Build and maintain end-to-end data pipelines across structured and unstructured datasets
• Develop imputation frameworks for missing or sparsely reported financial data (e.g., segment-level estimates, coverage gaps, timing mismatches)
• Design and implement data normalization and reconciliation logic across overlapping hierarchies (e.g., segments, geographies, entities)
• Perform data quality diagnostics, including coverage analysis, bias detection, and stability testing
• Partner with researchers to translate raw data into model-ready features
• Write efficient, reproducible code in Python and SQL for large-scale data processing
• Document methodologies clearly to ensure transparency and repeatability

• 2–5 years of experience in quantitative research, data science, or financial data engineering
• Strong expertise in data wrangling and transformation at scale (this is the core skill)
• Proven experience with missing data techniques and imputation methods (e.g., cross-sectional inference, time-series interpolation, model-based approaches)
• Advanced Python skills (pandas, numpy); strong SQL required
• Experience working with messy, real-world datasets (not just clean academic data)
• Solid grounding in statistics and econometrics
• Familiarity with equity markets and financial statements preferred
• You are an expert data wrangler — you know that 80% of the work is getting the data right
• You are skeptical of inputs and instinctively test for edge cases, leakage, and bias
• You understand that imputation is modeling, not a preprocessing afterthought
• You can balance statistical rigor with practical constraints (coverage vs. precision trade-offs)
• You write code that others can read, audit, and reuse
• Experience with NLP or unstructured data pipelines
• Exposure to alternative datasets (supply chain, transcripts, etc.)
• Familiarity with distributed compute environments (Databricks, Spark)
• Years of Experience:   12.00 Years of Experience

• Category          Name   Required          Importance       Experience
• Analytics         Python Yes                                         
• Software Skills SQL     Yes                 

• Health Benefits
• Referral Program
• Excellent growth and advancement opportunities