• Revenue analytics (AI-first): Build repeatable analyses for bookings and revenue performance including trends mix price-volume customer segment and product views variance drivers and business narratives for leadership.
• Expense analytics: Develop automated operating expense insights across functions including headcount spend categories and vendors highlighting anomalies run-rate shifts and savings opportunities; partner with FP&A on forecasts and scenarios.
• Forecasting and anomaly detection: Apply statistical and machine learning techniques such as time-series forecasting regression clustering and outlier detection to improve predictability and early-warning signals for revenue and expenses.
• Data preparation and metric definition: Curate document and maintain reliable datasets and finance definitions including source-of-truth metrics dimensional hierarchies and calculation logic to ensure consistency across reporting and AI outputs.
• Build in Snowflake and Oracle ecosystem: Write performant SQL and data transformations to integrate finance and operational data with preference for Snowflake and Oracle sources and optimize data models for analytics consumption.
• Agentic AI enablement: Package finance logic into agent-ready components with clear inputs outputs guardrails and evidence requirements that can be invoked by internal AI agents for standardized analyses.
• Model and output validation: Establish evaluation and QA methods for AI-generated analyses including accuracy checks reconciliation to finance systems regression tests on metrics and monitoring for drift before outputs reach stakeholders.
• Self-serve insights: Create lightweight tools and artifacts including notebooks templates metric catalogs and semantic layers that allow finance users to answer questions quickly and consistently.
• Stakeholder partnership: Work with Accounting FP&A Sales Ops IT Data Engineering and business leaders to prioritize use cases gather requirements and deliver measurable outcomes.
• Documentation and governance: Maintain clear documentation data lineage and control evidence for key datasets and models; support auditability and compliance expectations for financial analytics.

• Bachelors degree in a quantitative field such as Data Science Statistics Computer Science Engineering Economics Finance or similar; Masters degree preferred.
• 3 years of experience in analytics data science finance analytics or a related role with demonstrated ownership of end-to-end analytical deliverables.
• Strong SQL skills and experience working with large datasets with the ability to optimize queries and data models for performance and reliability.
• Hands-on experience with Python for data analysis and modeling with the ability to productionize analysis through reusable code and automation.
• Experience applying statistical and machine learning methods to business problems including forecasting driver analysis anomaly detection segmentation and experimentation or causal thinking where applicable.
• Ability to translate business questions into analytical approaches and communicate results clearly to non-technical stakeholders in written and verbal English.
• Strong finance fundamentals including P&L concepts revenue and expense drivers budgeting forecasting and variance analysis with comfort reconciling analytics to finance source systems.
• High standards for data quality controls and documentation; comfortable operating in environments where accuracy and auditability matter.
• It is not necessary to meet all job requirements to be a qualified candidate for the position.

• Demonstrates strong analytical rigor and a structured hypothesis-driven approach to solving business problems.
• Applies an engineering mindset with reusable code testing discipline and thoughtful automation.
• Uses AI pragmatically to accelerate work while maintaining disciplined evaluation controls and safety guardrails.
• Acts as a collaborative business partner across Finance Accounting IT Data Engineering and cross-functional stakeholders.
• Communicates complex analysis clearly and concisely translating technical outputs into executive-ready insights and action recommendations.
• Shows high ownership attention to detail and a commitment to auditability governance and data quality.
• Experience with Snowflake Oracle BI or semantic tools and GenAI or LLM-enabled analytics is a strong plus.

Learn More about SiTime:Review theGet to Know SiTimesection of our career page to explore our culture values and what makes us unique.

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