What you'll do:

This role sits within FP&A and operates as a bridge between Finance and technical teams. You will modernize and automate core finance workflows, improve forecast accuracy through statistical and machine learning approaches, and build durable tooling that increases the speed and quality of decision-making across the organization. This is a hands-on role for someone who is curious, pragmatic, and motivated to quickly and continuously improve financial operations.

• Automate and industrialize finance workflows: convert manual Excel-based processes into reliable, scalable automated pipelines and self-serve tools.
• Forecasting & modeling: develop and operationalize statistical and machine learning models to improve forecast precision, while balancing accuracy and explainability.
• Build FP&A tooling: create internal tools that make analyses repeatable, auditable, and easy to consume by Finance leadership and business partners.
• Partner cross-functionally: work closely with Finance teams (and technical partners as needed) to identify friction, quantify impact, and implement more efficient ways of operating.
• Data pipelines & rules: design and maintain rule-based engines and data pipelines that support forecasting, reporting, and decision workflows.
• Data storytelling: translate analyses and automations into clear narratives and meaningful visuals; communicate recommendations confidently internally.
• Responsible adoption of AI tooling: use LLM-powered development workflows (e.g., Claude Code) to accelerate delivery while maintaining quality, security, and auditability.

Who you are: 

• Strong SQL skills (writing, debugging, and optimizing queries against large datasets).
• Advanced Excel skills and fluency in common FP&A workflows (structured modeling, reconciliation, scenario analysis).
• Working Python literacy (ability to write production-quality scripts and notebooks; comfort with packaging, testing, and reproducibility).
• Experience working in a Git-based workflow (branching, pull requests/code review, version control hygiene).
• Proven ability to take ambiguous problems and drive them to outcomes, identifying opportunities, scoping work, executing, and iterating.
• Comfort operating in a role with direct financial impact, including attention to controls, data quality, and auditability.
• Excellent communication skills (written and verbal), including the ability to tailor technical detail to the audience.

Preferred Qualifications:

• 2 - 4 years working experience as in Analytics, Finance, or AdTech
• Experience with Python data and visualization tooling such as pandas and plotly. 
• Experience building lightweight applications for analysts and business users (e.g., Streamlit, Dash, or similar).
• Familiarity with distributed processing and large-scale data (e.g., PySpark).
• Experience implementing agentic workflows and/or automation in a business context.
• Comfort using LLM tools (e.g., Claude Code) to speed up development, analysis, documentation, and iteration.
• Exposure to programmatic advertising / AdTech concepts (forecasting, seasonality, supply dynamics, fees/take rates, identity, etc.).

The Trade Desk does not accept unsolicited resumes from search firm recruiters. Fees will not be paid in the event a candidate submitted by a recruiter without an agreement in place is hired; such resumes will be deemed the sole property of The Trade Desk. The Trade Desk is an equal opportunity employer. All aspects of employment will be based on merit, competence, performance, and business needs. We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, veteran status, or any other status protected under federal, state, or local law.

Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.