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212 Financial Analysis Jobs in United States

Every forecast asks for another layer of variance testing, another model rebuilt when the assumptions shift mid-quarter.

Financial Analysis jobs by city

• Wrocław26
• London20
• Bengaluru18
• São Paulo16
• Jakarta16
• Budapest16
• Munich14
• Warsaw14
• Hong Kong14
• Shanghai13
• Des Moines13
• Sofia13
• Paris13
• Hyderābād13
• Poznań13
• Mexico City12

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• Accounting Support1746
• Banking and Lending1391
• Financial Managers1261
• Accounting Professionals1219
• Financial Compliance678
• Tax Specialists548
• Investment Specialists429
• Insurance Claims and Sales346
• Financial Sales257

Questions

• What does a typical day in Financial Analysis involve?

  A typical day involves building or updating financial models, analyzing variance between actuals and forecasts, and preparing reports for management. The work shifts between deep spreadsheet work, gathering data from different departments, and meetings where findings get discussed and challenged.

• Can someone enter Financial Analysis without a finance degree?

  Yes, many analysts enter with degrees in economics, accounting, mathematics, or business, and some transition from operational roles with strong analytical components. What matters more than the specific degree is comfort with quantitative work and the ability to learn financial modeling and accounting principles quickly.

• How does Financial Analysis differ from accounting?

  Financial Analysis focuses on forward-looking forecasts, business performance, and decision support, while accounting concentrates on recording historical transactions and ensuring compliance. Analysts interpret what the numbers mean for future strategy; accountants ensure the numbers are accurate and properly classified.

• What skills improve most quickly in Financial Analysis roles?

  Modeling speed and the ability to spot patterns in variance improve fastest, usually within the first year. The skill of translating complex analysis into clear recommendations for non-finance stakeholders develops more gradually through repeated presentations and feedback.

• How much of Financial Analysis work happens in meetings versus solo?

  Most time goes to solo work building models and analyzing data, typically 60-70% of the week. The rest involves meetings to gather inputs, present findings, or defend assumptions when forecasts get challenged by business leaders.

• What makes Financial Analysis work stressful?

  Month-end and quarter-end close periods compress timelines severely, requiring fast turnarounds on variance explanations while executives wait. The stress also comes from defending forecasts that prove wrong, and from urgent requests that disrupt planned work when leadership needs immediate analysis.

• Is Financial Analysis heavily deadline-driven?

  Yes, the work operates around fixed reporting cycles tied to monthly close, quarterly earnings, and annual planning processes. These deadlines are immovable, which means late nights increase predictably during close periods and budget season.

• How does Financial Analysis work change with seniority?

  Junior analysts spend most time in spreadsheets executing models others designed, while senior analysts build frameworks, choose methodologies, and present directly to executives. The work shifts from technical execution toward judgment calls about which scenarios to model and how to frame recommendations.

• What part of Financial Analysis work takes the longest to learn?

  Understanding the business drivers behind the numbers takes years, not months, because it requires seeing multiple cycles and learning how different functions interact. Technical modeling skills develop relatively quickly, but knowing which assumptions deserve scrutiny comes only with accumulated context.

• Do Financial Analysis roles require programming or coding?

  Most roles do not require programming, though SQL for data extraction and Python or R for automation increasingly help. Excel remains the primary tool, but familiarity with business intelligence platforms and willingness to learn scripting make analysts more efficient as data volumes grow.