Centraprise
Data Scientist
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Data Scientist (Quantitative Research Analyst) on a contract basis in Chicago, IL or Boston, MA. Requires 2–5 years of experience in quantitative research, strong data wrangling skills, advanced Python, and SQL expertise.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
May 9, 2026
🕒 - Duration
Unknown
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🏝️ - Location
On-site
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📄 - Contract
Unknown
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🔒 - Security
Unknown
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📍 - Location detailed
Chicago, IL
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🧠 - Skills detailed
#Statistics #Data Science #"ETL (Extract #Transform #Load)" #Pandas #Data Wrangling #SQL (Structured Query Language) #Python #NumPy #Data Engineering #Data Modeling #Datasets
Role description
Job Title : Data Scientist (Quantitative Research Analyst (Data Modeling & Imputation)
Employment Type : Contract
Location: Chicago, ILor Boston, MA (Onsite)
Job Description :
Required Experience
• 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
Job Title : Data Scientist (Quantitative Research Analyst (Data Modeling & Imputation)
Employment Type : Contract
Location: Chicago, ILor Boston, MA (Onsite)
Job Description :
Required Experience
• 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
