Quantitative Research Analyst (Data Modeling & Imputation)

Job Title: Quantitative Research Analyst (Data Modeling & Imputation)

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

Employment Type: Contract

Pay Range: $70.30/hr. On W2 | $84.21/hr. on C2C

Domain: Quantitative Finance & Data Engineering

Application Deadline: July 15, 2026

SKILLS REQUIRED

Primary (Must-Have):

✔️Expert Data Wrangling: Mastery of large-scale data transformation and pipeline development.

✔️Advanced Statistical Modeling: Expert-level experience in imputation methods (cross-sectional inference, time-series interpolation, model-based approaches).

✔️Technical Stack: Advanced Python (Pandas, NumPy) and strong SQL proficiency.

✔️Data Integrity: Proven ability to perform quality diagnostics, bias detection, and stability testing.

✔️Financial Domain Knowledge: Strong grounding in statistics, econometrics, and familiarity with equity markets/financial statements.

Secondary (Good to Have):

✔️Advanced Analytics: Experience with NLP and unstructured data pipelines.

✔️Big Data Tools: Familiarity with distributed computing (Databricks, Spark).

✔️Alternative Data: Exposure to supply chain data, transcripts, or other non-traditional datasets.

POSITION OVERVIEW

We are seeking a highly skilled Quantitative Research Analyst specializing in data modeling and imputation to join our team in Chicago or Boston. This role is for a seasoned "data wrangler" who understands that the foundation of any quantitative model is the integrity of the data itself. You will be responsible for building robust imputation frameworks, normalizing complex financial datasets, and ensuring that our research models are built on clean, reliable, and bias-tested features.

ROLES & RESPONSIBILITIES

✔️Pipeline Architecture: Build and maintain end-to-end data pipelines for both structured and unstructured financial datasets.

✔️Imputation Frameworks: Design and implement sophisticated imputation strategies to address coverage gaps, segment-level estimates, and timing mismatches.

✔️Normalization & Reconciliation: Develop logic to normalize data across overlapping hierarchies (geographies, entities, and segments).

✔️Rigorous Diagnostics: Conduct deep-dive data quality analysis, including coverage assessments, bias detection, and stability testing.

✔️Research Partnership: Collaborate directly with researchers to translate raw, messy real-world data into model-ready features.

✔️Code Excellence: Write efficient, reproducible, and auditable code in Python and SQL; document all methodologies to ensure complete transparency.

BENEFITS

Medical | Dental | Vision | 401(k)

EEOC Compliance

We are an equal opportunity employer, and all qualified applicants will receive consideration for employment.

DISCLAIMER

AI Usage Policy: Pacer Group uses AI to assist in screening applications. Final hiring decisions are made by human recruiters based on qualifications and experience.