Role Scope 

The Data Research Specialist is an individual contributor role that develops deep proficiency in a niche dataset/workflow and owns the end-to-end data lifecycle for one or more datasets —from sourcing and rule design through validation, enrichment, delivery, and handover. The role moves beyond execution into designing how data is treated, with a measurable downstream impact on data quality, efficiency, and usability. 

Specialists maintain and evolve rule books and SOPs, provide UAT inputs, participate in solutioning, and begin direct interactions with vendors and internal partners. The role requires strong domain understanding, disciplined governance, and the ability to balance quality, speed, and risk while contributing to automation- and AI-enabled improvements. 

What You’ll Do (Key Responsibilities) 

Operational Delivery 

• Collect, analyze, and validate US GAAP–based financial statement data (Balance Sheet, Income Statement, Cash Flow, and disclosures) from regulatory filings, earnings reports, and company publications 

• Interpret complex financial disclosures and ensure accurate mapping, classification, and standardization of financial data in line with Compustat methodologies 

• Perform detailed financial statement analysis, including cross‑statement checks, trend analysis, and reasonableness validation 

• Ensure data integrity, consistency, and timeliness across financial datasets by following established research guidelines and quality standards  

• Own the end-to-end data lifecycle for assigned datasets, including source selection, ingestion checks, rule application, validation and enrichment, publishing, and handover. 

• Ensure SLAs for timeliness, coverage, and quality are met; track end-to-end KPIs, exception trends, and data drift indicators. 

• Maintain and update rule books and SOPs as requirements evolve; communicate changes and readiness criteria to stakeholders. 

• Support onboarding new datasets or sources through playbooks, sample evaluations, and readiness assessments. 

• Participate in UAT cycles, providing domain test cases, acceptance criteria, and defect reproduction steps. 

Process Excellence 

• Keep documentation, runbooks, controls, and SOPs audit-ready, ensuring strong handovers and traceability. 

• Contribute risk assessments for changes in rules, sources, or workflows, including rollback and monitoring plans. 

• Partner with Technology and Process Experts to design and optimize automation- and AI-enabled workflows under governance. 

• Apply working knowledge of LLM/GenAI tools for extraction, validation, and quality checks. 

• Run small pilots and experiments (e.g., rule optimization, automation scripts, sampling) to improve efficiency, quality, and coverage. 

• Provide SME inputs for data analysis, lineage documentation, and downstream integrations. 

Stakeholder Management 

• Act as the point of contact for assigned datasets with peers, Data Stewards, cross-domain teams, Data Automation & Transformation, Quality teams, representing rule decisions within defined governance. 

• Support seniors and other stakeholders by providing domain inputs to requirements, validating fixes, and refining data workflows. 

• Begin liaising with vendor partners on clarifications, escalations, and data quality issues, supporting gap resolution actions. 

• Support resolution of client queries/escalations with clear, data-backed responses; contribute to knowledge-sharing forums and working groups. 

• Provide informal guidance to Interns through onboarding support as a buddy or micro-trainings (no formal people management). 

How Impact Will Be Measured (Success Metrics) 

• Dataset quality KPIs (accuracy, completeness, consistency); reduced exceptions and cycle time 

• Timely resolution of client/vendor issues; improved satisfaction 

• Quality/maintainability of rule books and SOPs (audit outcomes; update cadence) 

• Measurable efficiency and quality gains from UAT, automation, and process improvements. 

For This Role You'll Need to Have (Skills & Competencies)  

Technical Skills 

• Advanced Excel and solid SQL for investigations, reconciliations, and audits. 

• Working knowledge of Python or similar scripting for basic automation and data checks (preferred). 

• Familiarity with LLM/GenAI tooling and prompt design, including evaluation of metrics and guardrails. 

• Understanding vendor data models and ingestion pipelines, with awareness of upstream/downstream impacts. 

• Awareness of internal platforms and data ecosystems (e.g., data lakes, workflow engines). 

Functional Skills 

• Strong understanding of US GAAP financial statements (Balance Sheet, Income Statement, Cash Flow Statement) and their interrelationships 

• Ability to analyze and interpret financial disclosures, footnotes, and management commentary 

• Strong analytical skills, attention to detail, and ability to work with large and complex financial datasets 

• Rule design and maintenance governing data treatment, enrichment, and delivery. 

• Ability to resolve complex tickets and client queries independently. 

• Support definition and refinement of data KPIs and control checks. 

Behavioral Skills 

• Strong problem-solving and analytical thinking, with comfort navigating ambiguity within defined scope. 

• Curiosity to understand downstream use cases and client impact, translating insights into rule or control improvements. 

• Clear communication and ability to collaborate and influence within the assigned dataset or domain. 

• Ownership mindset with ability to work with limited supervision and maintain disciplined documentation. 

• Resilience under pressure, handling escalations with sound judgment on quality, timeliness, and defined risk controls. 

• Demonstrates strong learning agility and digital acumen, with a continuous learning mindset and the ability to adapt to evolving tools and technologies. 

Preferred Experience 

• 2–4 years of experience in data research, data operations, or market/financial data roles. 

Key Interfaces & Stakeholders 

• Associates and Specialists within Data Research & Operations. 

• Data Stewards, Data Automation & Transformation, and Data Quality teams. 

• Technology team which is responsible for platforms, tools, and pipelines. 

• External vendors and, where applicable, clients via support channels.