Data AnalystData Scientist

Tek Ninjas

Job Location:

Chicago, IL - USA

Monthly Salary: Not Disclosed

Posted on: 30+ days ago

Vacancies: 1 Vacancy

Job Summary

Job Title: Market Intelligence Analyst (Data Modeling & Imputation)

Location: Chicago IL and Boston MA
Duration: 12 months

Role Overview

As a Market Intelligence Analyst specializing in Data Modeling & Imputation you will play a critical role in transforming fragmented noisy financial data into high-fidelity research-ready assets. Your primary focus will be on the technical architecture of our datasets-addressing coverage gaps and ensuring that our quantitative signals are built on a foundation of statistically robust and economically sound data.

Key Responsibilities

Advanced Data Imputation: Construct validate and deploy sophisticated imputation methodologies to resolve missing or incomplete financial data points.
Dataset Engineering: Develop and maintain high-quality datasets that are ready for immediate use in production-level market research and signal generation.
Pipeline Architecture: Design and implement end-to-end data pipelines that automate the flow of information from raw fragmented sources to refined outputs.
Data Reconciliation: Manage and reconcile complex hierarchical financial data structures to ensure consistency across different levels of reporting.
Quality Assurance: Guarantee the stability and statistical robustness of data outputs ensuring they meet the rigorous standards required for downstream quantitative research.
Coverage Expansion: Proactively identify and bridge data coverage gaps to enhance the depth and breadth of our market intelligence.

Required Skills & Qualifications

Statistical Modeling: Strong expertise in statistical techniques and machine learning models specifically applied to data imputation and predictive modeling.
Financial Literacy: A deep understanding of financial statements market structures and hierarchical financial data.
Data Engineering: Proficiency in building scalable data pipelines (e.g. Python SQL Spark) and working with large-scale unstructured datasets.
Analytical Rigor: The ability to validate models not just on statistical fit but on economic logic and real-world market behavior.
Problem Solving: A proven track record of handling fragmented or messy data and turning it into a structured reliable product.

Job Title: Market Intelligence Analyst (Data Modeling & Imputation) Location: Chicago IL and Boston MA Duration: 12 months Role Overview As a Market Intelligence Analyst specializing in Data Modeling & Imputation you will play a critical role in transforming fragmented noisy financial data into ...

Job Title: Market Intelligence Analyst (Data Modeling & Imputation)

Location: Chicago IL and Boston MA
Duration: 12 months

Role Overview

Key Responsibilities

Advanced Data Imputation: Construct validate and deploy sophisticated imputation methodologies to resolve missing or incomplete financial data points.
Dataset Engineering: Develop and maintain high-quality datasets that are ready for immediate use in production-level market research and signal generation.
Pipeline Architecture: Design and implement end-to-end data pipelines that automate the flow of information from raw fragmented sources to refined outputs.
Data Reconciliation: Manage and reconcile complex hierarchical financial data structures to ensure consistency across different levels of reporting.
Quality Assurance: Guarantee the stability and statistical robustness of data outputs ensuring they meet the rigorous standards required for downstream quantitative research.
Coverage Expansion: Proactively identify and bridge data coverage gaps to enhance the depth and breadth of our market intelligence.

Required Skills & Qualifications

Statistical Modeling: Strong expertise in statistical techniques and machine learning models specifically applied to data imputation and predictive modeling.
Financial Literacy: A deep understanding of financial statements market structures and hierarchical financial data.
Data Engineering: Proficiency in building scalable data pipelines (e.g. Python SQL Spark) and working with large-scale unstructured datasets.
Analytical Rigor: The ability to validate models not just on statistical fit but on economic logic and real-world market behavior.
Problem Solving: A proven track record of handling fragmented or messy data and turning it into a structured reliable product.