FP&A Expert - Financial Forecaster

Obsidian; Sydney; On-site; AUD 120,000 - 180,000; Full time

Job summary

Obsidian is seeking a Domain Expert – FP&A (Financial Forecaster) to oversee AI-driven forecasting tasks. You will assess model outputs, verify accuracy against underlying accounts and coordinates, and ensure grading guidance remains rigorous and fair.

The role requires deep FP&A expertise and hands‑on experience with forecasting models. The ideal candidate has 5+ years in FP&A, financial reporting, or related fields, with strong model-reading skills and the ability to spot inconsistencies

Qualifications

• Must have 5+ years in FP&A, financial reporting, or related roles.
• Experience with financial forecasting, planning, and variance analysis.
• Ability to read and validate model outputs and identify discrepancies.

Responsibilities

• Review and QA AI-agent rollouts against model data and documents.
• Identify failure modes and flag tasks that don’t meet FP&A standards.
• Improve realism and difficulty of tasks to reflect authentic FP&A work.
• Update grading guidance for accuracy and consistency in evaluations.
• Catch plausible-but-wrong answers that could mislead reviewers.

Skills

Forecasting

Modeling

Scenario analysis

Data validation

GAAP knowledge

Attention to detail

Education

Bachelor's degree in Finance or Accounting

Tools

Excel

SQL

Financial Forecaster

Job description

Domain Expert – FP&A (Financial Forecaster)

To qualify you must use Financial Forecaster regularly, weekly or more, as a working part of your job, with 5+ years of professional FP&A experience as an FP&A analyst/manager, financial reporting or technical accounting analyst, lender-reporting or treasury analyst, or controller.

Financial Forecaster knowledge we require

Working the model

• Multidimensional models: navigating models, dimensions and account hierarchies, and reading a cube slice at an exact coordinate rather than at whatever grain the UI lands on

• Scenarios: creating one, setting its parent, knowing what it inherits versus holds in its own cells, and locking it — plus identifying the forecast of record by its lock or acceptance date

• Accounts and assumptions: telling an input account from a calculated one, and knowing when to fix a figure by editing the account formula versus overriding an assumption cell, because the two are not interchangeable and the grader can tell

• CSV imports: loading actuals and driver data, and the unit and scale discipline an import demands

• Note surfaces: scenario notes, driver notes, policy notes, planning notes, assumption change logs and variance commentary — and which one a given statement belongs in

• Knowing what Financial Forecaster cannot do, so you can recognize a task that asks for a figure or a dimension member the model will never produce

Reading models & packs

• Reading a scenario fluently: what is an input versus a calculated account, which cells are overridden, what a scenario inherits from its parent versus holds itself

• Reading a reporting pack against the model behind it: tracing a printed figure back to the account, scenario and coordinate it came from, and telling a real tie-out from a coincidence

• Recognizing the vintage that governs: which scenario is the forecast of record, when it was locked or accepted, and why a later forecast does not silently supersede an approved one

• Decoding a coordinate (Time, Entity, Department, Product/Product Line, Region, and facility dimensions like Debtor or Aging Bucket) and knowing what a figure means at that grain versus aggregated

Finding & tying out the number

• Navigating account hierarchies and scenario trees to locate the figure that actually drives a covenant, a KPI, or a disclosure — targeted retrieval instead of scrolling the model

• Unit and scale traps: thousands-versus-dollars mismatches, a cost base that cannot be reconciled to its own revenue, per-period versus LTM versus fiscal-year-to-date, and sniffing out a figure that is off by 10× or 1,000×

• Reconciling two in-world sources that disagree, and judging which one governs

Entity structure & governing documents

• The reporting landscape: legal entity versus department versus segment versus consolidated, and which level a given obligation is measured at

• Credit agreement mechanics: the provision fixing the accounting basis (frozen GAAP), Test Periods and test dates, fixed charge coverage and total net leverage, Consolidated EBITDA add-backs and their caps, Consolidated Funded Indebtedness, borrowing base and Eligible Accounts, redetermination, and who must sign a compliance certificate

• Case relationships in the financial sense: parent and child scenarios, consolidated versus entity results, and how an amendment, consent or waiver changes the measure going forward

Measure types & classification

• GAAP versus non-GAAP versus covenant measures, and why the same label can mean three different numbers in one pack

• Non-GAAP discipline: exclusions requiring a written determination that an amount is not normal and recurring, reconciliation to the nearest GAAP measure, equal prominence, and restating prior periods when a metric definition changes

• Forecast versus projection under the AICPA PFI guidance, range presentations whose endpoints must be comparably likely, and what a reasonably objective basis requires

• The standards that decide the answer: going concern and the look-forward window (ASC 205-40), operating segments and the CODM (ASC 280), materiality screening (SAB 99 / Topic 1M), KPI disclosure (Release 33-10751), known trends in MD&A (Reg S-K Item 303), valuation allowances as a discrete item versus through the annual effective rate

Documents, records & workflow

• Document types: compliance certificate, borrowing base certificate, lender package, board or committee pre-read, earnings release and reconciliation, disclosure committee memo, workpaper, and what each is for

• What a finished deliverable requires versus what merely describes it: a correction stated in a memo is not the same as the correction applied in the file or written to the model

• App workflow surfaces: scenario creation and locking, saved views, and exports for large result sets

What you'll do

• Review and QA AI-agent rollouts: work through completed agent runs in Financial Forecaster and judge whether the agent's answer is correct against the actual model and documents — right scenario, right account, right coordinate, right basis, right conclusion, right deliverable

• Identify failure modes: pinpoint where and why an agent succeeds too easily or fails wrongly, and flag tasks that are too easy or solvable without actually using Financial Forecaster

• Improve realism and difficulty: refine the task prompt and the seeded world so tasks reflect authentic FP&A and lender-reporting work and are genuinely hard for a capable agent

• Update grading guidance for accuracy: sharpen how answers are graded so correct answers pass and wrong ones fail, including gating failures (wrong scenario or vintage used, wrong accounting basis, an add-back taken that the agreement excludes, a figure stated but never written to the model, or a conclusion transcribed from a document that already gave it away)

• Catch plausible-but-wrong answers that a non-expert reviewer would let slide — a number that looks right at the wrong grain, or a conclusion that is correct in isolation and inconsistent with the rest of the pack

Requirements

• Frequent hands-on Financial Forecaster use for real forecasting, covenant or reporting work in your current or recent role, weekly or more

• 5+ years in FP&A, financial reporting, technical accounting, or lender reporting: FP&A analyst/manager, financial reporting analyst, technical accounting analyst, treasury/lender-reporting analyst, or controller

• Can read a Financial Forecaster model on sight: tell inputs from calculations, spot an override, identify the governing scenario and its vintage, and judge whether a figure ties at the grain it is stated at

• Reflexive with Financial Forecaster's scenario and import mechanics, and with the scale and basis traps that produce confidently wrong numbers

• Sound judgment on what a correct, complete answer looks like, and the confidence to mark a plausible answer wrong

• Bonus: credit agreement compliance and borrowing base reporting; going concern or impairment analysis; segment reporting; SEC reporting or disclosure committee experience; hands-on experience across multiple models and entities, not just one company's single model

• Note: a CPA, CFA, or MBA is welcome but not required; experienced FP&A analysts and technical accounting specialists are the primary pool