Quantitative Research Analyst (Data Modeling & Imputation)

The Role: Quantitative Research Analyst (Data Modeling & Imputation)
Location: Chicago, IL or Boston, MA (Onsite)

Job Description

Value Technology focuses on building high-quality, research-ready datasets from incomplete, inconsistent, and fragmented financial data. You will be responsible for designing and implementing imputation frameworks, data transformations, and modeling pipelines that convert raw inputs into reliable signals.

This is not a pure modeling role. The majority of the value comes from data construction, validation, and methodological rigor upstream of the model.

Skill Matrix:

·         SQL - 14+ Years 

·         Python - 5+ Years 

·         Statistics and Econometrics - 5+ Years 

·         DA - 10+ Years 

·         DS - 5 Years 

·         Quant - 5 Years

·         Finance - 10+ Years

Data Product Manager | Data Governance, Data Quality & Enterprise Data Management

Professional Summary

·         14+ years of experience in the Banking IT sector, transforming data into actionable insights.

·         10+ years as a Data Analyst specializing in banking, marketing analytics (including card analytics) for top U.S. banks.

·         7+ years leading cross-functional teams of Data Engineers, Data Analysts, and Business Stakeholders, bridging the technical and business worlds by translating complex data concepts into understandable solutions while ensuring compliance, security, and privacy standards.

·         CFA Level III candidate (August 2026) and Certificate in Quantitative Finance (enrolled)—bringing deep financial analysis expertise and sector knowledge to data-driven decision making

·         Masters in Business Analytics from the University of Minnesota (Carlson School of Management).

·         Cleaned and standardized customer address records using postal standards, improving deliverability and reducing returned mail by resolving incomplete, duplicate, and non‑conforming entries.

·         Sector expertise in financial services with deep understanding of financial statements, equity research, and industry analysis—experience analyzing company financials, broker estimates, and market-moving data for Fortune 500 banking clients

·         Expertise in defining data product strategy and roadmap, overseeing data quality metrics and KPIs, and managing data governance processes across enterprise data platforms. Strong skills in stakeholder management and collaboration with Data Engineers, Data Analysts, Front Office, Risk, Compliance, and Technology teams in fast-paced environments. 

·         Proficient in SQL, Python, PySpark, Snowflake, and Azure/Databricks for data engineering and pipeline development. Experienced in developing and implementing data governance policies, data quality standards, and compliance frameworks. Skilled in Agile methodologies, CI/CD pipelines, and presenting data product strategy to C-suite executives and business stakeholders. 

·         Data Visualization and Reporting: Strong capabilities in crafting Interactive dashboards, reports, KPIs, charts, maps, drill-down, bookmarks, row-level security capabilities, data exploration, storytelling, sharing, alerts, subscriptions, apps, and collaboration.

·         Experience in SQL development, including database creation, queries, and stored procedures in Microsoft SQL Server.

·         Proficient in Tableau for data preparation, visualization, dashboarding, and storytelling development.

·         Strong experience in migrating other databases to Snowflake.

·         Worked on Dimensional Data modelling in Star and Snowflake schemas and Slowly Changing Dimensions (SCD).

·         Excel Functionality: Proficient in Excel''s data processing functionalities, including VLOOKUPs, pivot tables, and Power Query.

·         Data Mining, Modeling and Database Design: Deep expertise in data mining, modeling, star, and snowflake schemas, and optimizing database performance.

Skills