Finance Data Analyst

About SiTime

SiTime is the Precision Timing company. Timing is the heartbeat of all electronics, ensuring performance, resilience and scalability. For decades, quartz devices, non-silicon technology, have kept systems in sync, but they struggle in harsher, more demanding environments. MEMS-based Precision Timing delivers greater accuracy, smaller size and resilience. Today, MEMS timing powers over 400 applications, including high-growth ones in AI datacenters, automated driving, industrial and humanoid robots, wearables and IoT.Our semiconductor MEMS programmable solutions offer a rich feature set that enables customers to differentiate their products with higher performance, smaller size, lower power, and better reliability. With more than 4 billion devices shipped, SiTime is changing the timing industry.

Job Summary

SiTime is seeking a Finance Data Analyst / Data Scientist in Ukraine to build AI-powered analytics that improve visibility, forecasting, and decision-making across revenue and operating expenses. This role will partner closely with Finance leadership and cross-functional stakeholders to translate business questions into scalable data products, ranging from curated datasets and semantic layers to advanced models and agent-ready tools. A key part of the role is enabling Finance AI initiatives by preparing trusted datasets, defining metrics and business logic, creating validation approaches, and supporting safe automation for analyses used in close, forecast, and executive reporting. It is not necessary to meet all job requirements to be a qualified candidate for the position.

Responsibilities

• 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.
  
  

Qualifications & Requirements

• 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.
  
  

Desired Characteristics & Attributes

• 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