What are the responsibilities and job description for the Data Scientist, Enterprise Data Technology position at Balyasny Asset Management?
Description:
Balyasny Asset Management is seeking a Data Scientist to join the Enterprise Data Technology team. Our team builds trusted, investment-ready datasets and data-quality systems that support investment teams.
This role combines data science, data engineering, and product ownership. You will work directly with portfolio teams, analysts, researchers, and quantitative investors to understand how they use data in their workflows. You will translate those needs into scalable data products, quality controls, and monitoring systems that make research and reference data more reliable, discoverable, and useful.
Responsibilities
Partner with users to understand research workflows, data dependencies, and quality requirements.
Own datasets and data products end to end: discovery, requirements, vendor evaluation, onboarding, validation, documentation, support, and ongoing enhancement.
Analyze source data to assess coverage, completeness, timeliness, consistency, outliers, missing values, schema changes, and other quality risks.
Design and build automated data quality checks, monitoring, alerting, reconciliation, and reporting workflows.
Evaluate new vendor datasets and determine their suitability for investment research workflows.
Collaborate with data engineers, data managers, and platform teams to deliver robust, well documented production datasets.
Communicate with external data providers to investigate issues, understand methodologies, and define integration requirements.
Act as a product owner for supported data domains, prioritizing improvements based on user impact and data-quality risk.
Qualifications
Experience in data science, data engineering, analytics, or a related quantitative field.
Strong Python and SQL skills; experience analyzing large, complex datasets.
Familiarity with financial instruments, security identifiers, reference data, market-data vendors, and/or investment research workflows.
Demonstrated ability to investigate data issues, build quality systems, and communicate findings clearly.
Ability to manage ambiguous problems independently while collaborating closely with technical and investment stakeholders.
Strong written and verbal communication skills and exceptional attention to detail.
Balyasny Asset Management is seeking a Data Scientist to join the Enterprise Data Technology team. Our team builds trusted, investment-ready datasets and data-quality systems that support investment teams.
This role combines data science, data engineering, and product ownership. You will work directly with portfolio teams, analysts, researchers, and quantitative investors to understand how they use data in their workflows. You will translate those needs into scalable data products, quality controls, and monitoring systems that make research and reference data more reliable, discoverable, and useful.
Responsibilities
Partner with users to understand research workflows, data dependencies, and quality requirements.
Own datasets and data products end to end: discovery, requirements, vendor evaluation, onboarding, validation, documentation, support, and ongoing enhancement.
Analyze source data to assess coverage, completeness, timeliness, consistency, outliers, missing values, schema changes, and other quality risks.
Design and build automated data quality checks, monitoring, alerting, reconciliation, and reporting workflows.
Evaluate new vendor datasets and determine their suitability for investment research workflows.
Collaborate with data engineers, data managers, and platform teams to deliver robust, well documented production datasets.
Communicate with external data providers to investigate issues, understand methodologies, and define integration requirements.
Act as a product owner for supported data domains, prioritizing improvements based on user impact and data-quality risk.
Qualifications
Experience in data science, data engineering, analytics, or a related quantitative field.
Strong Python and SQL skills; experience analyzing large, complex datasets.
Familiarity with financial instruments, security identifiers, reference data, market-data vendors, and/or investment research workflows.
Demonstrated ability to investigate data issues, build quality systems, and communicate findings clearly.
Ability to manage ambiguous problems independently while collaborating closely with technical and investment stakeholders.
Strong written and verbal communication skills and exceptional attention to detail.