About the Role:

Grade Level (for internal use):

The Team:

The Data/Content Management team delivers and maintains accurate, complete, and timely financial datasets while leveraging cutting-edge AI and automation technologies. Our team of technical data analysts works on various research reports and company documents to collect information and generate meaningful insights from data. We support Compustat Fundamentals and other critical business lines, focusing on innovation through AI/LLM tools and advanced analytics to meet our clients' evolving needs.

The Impact:

We provide the highest quality content that is essential for our clients to make decisions with conviction. As a Lead Data Analyst, you will support the integrity and comprehensiveness of datasets by utilizing advanced technical tools including Python, SQL, and AI/LLM technologies to automate data collection, analysis, and standardization processes from various sources including regulatory documents, earnings reports, and market publications.

What's in it for you:

Work with cutting-edge AI and machine learning technologies in the financial data space

Develop advanced technical skills in Python programming, SQL database management, and AI/LLM implementation

Gain comprehensive understanding of global financial markets while building automated solutions

Opportunity to lead process improvement and automation projects that directly impact business efficiency

Collaborate with cross-functional teams on innovative data solutions and emerging technologies

Responsibilities:

• Design and implement automated data collection workflows using Python and AI/LLM tools to extract, process, and standardize financial data from various sources including regulatory filings, earnings reports, and market documents

• Develop and maintain SQL queries and database solutions to ensure data integrity, accuracy, and efficient retrieval for Compustat Fundamentals and other financial datasets

• Leverage large language models and natural language processing techniques to analyze unstructured financial documents, extract key business insights, and automate data classification processes

• Build and optimize Python-based data pipelines that integrate with existing research tools and databases, reducing manual effort while improving data quality and processing speed

• Collaborate with cross-functional teams to identify opportunities for AI-driven process improvements and implement technical solutions that enhance research efficiency and accuracy

• Monitor and troubleshoot automated systems, ensuring consistent performance and implementing enhancements based on evolving business requirements and emerging AI technologies

Compensation/Benefits Information:

S&P Global states that the anticipated base salary range for this position is $60,000 to $75,000. Final base salary for this role will be based on the individual’s geographic location, as well as experience level, skill set, training, licenses and certifications. In addition to base compensation, this role is eligible for an annual incentive plan. This role is eligible to receive additional S&P Global benefits. For more information on the benefits we provide to our employees, please click here.

What We're Looking For:

Basic Required Qualifications:

• Bachelor's or Master's degree in Computer Science, Data Science, Finance, or related technical field with 1-3 years relevant experience

• Strong programming skills in Python with experience in data manipulation libraries such as pandas, numpy, or similar frameworks

• Proficiency in SQL and database management systems including query optimization and data modeling techniques

• Experience with AI/LLM tools and frameworks such as OpenAI APIs, Hugging Face, LangChain, or similar natural language processing technologies- Solid understanding of financial statements (Balance Sheet, Income Statement, Cash Flow) and their interrelationships

• Strong analytical and problem-solving skills with attention to detail and ability to work with large, complex datasets

Additional Preferred Qualifications:

• Experience with machine learning frameworks such as scikit-learn, TensorFlow, or PyTorch for data analysis and automation

• Knowledge of cloud platforms such as AWS, Azure, or Google Cloud for data processing and AI model deployment

• Familiarity with data visualization tools like Tableau, Power BI, or Python libraries such as matplotlib and seaborn