• 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.

• Bachelor’s degree in a quantitative field such as Data Science, Statistics, Computer Science, Engineering, Economics, Finance, or similar; Master’s 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 the Get to Know SiTime section of our career page to explore our culture, values, and what makes us unique.

• Innovation on Top – Philosophies of Innovation with Rajesh Vashist
• Fabrication Knowledge – An Interview with Rajesh Vashist
• SiTime Corporation – YouTube

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