Job Title: Senior Data Analyst

Company Name: iOPEX Technologies

Job Url: https://chatgpt.com/c/698d23c9-016c-832f-b916-8aa954de4818

Job Description: If a senior stakeholder asked for this on Wednesday for a Monday board presentation, I’d treat it as both an analytics and risk-management exercise.

Day 1 (Wednesday): Clarify and scope
Clarify the decision context

How the forecast will be used (scenario planning vs. committed target)

Required level of precision

Definition of “avoided spend” and what counts as an overpayment

Whether a range is acceptable vs. a point estimate

Align on output

Point estimate + confidence range

Key assumptions

Sensitivity scenarios

I’d communicate early that given data limitations, I’ll provide a defensible estimate with transparent assumptions rather than false precision.

Day 2 (Thursday): Build a defensible baseline
Assemble the best available proxy dataset

Pull historical payment and overpayment records from all relevant systems

Reconcile definitions and identify gaps

Partner with finance/operations to validate logic

Data validation

Reconcile totals against financial statements or summaries

Quantify missingness and potential bias

Document transformation logic

Initial analysis

Trend historical overpayment rates

Identify drivers (volume, policy changes, operational changes)

Create a simple baseline projection

Day 3 (Friday): Model scenarios
Given limited time and imperfect data, I’d prioritize transparent forecasting approaches, such as:

Baseline trend extrapolation

Scenario modeling (conservative, expected, optimistic)

Sensitivity analysis on key drivers (volume, detection rate, intervention impact)

I’d quantify uncertainty explicitly (ranges rather than single numbers).

Day 4 (Weekend or Monday morning): Synthesize for decision-makers
Deliver a concise executive summary:

1. The estimate
Range + most likely scenario

2. Key drivers
What assumptions matter most

3. Confidence level
What is well supported vs. directional

4. Risks & caveats
Data gaps and potential variance

5. Next steps
How to improve the forecast (data instrumentation, monitoring plan)

How I’d communicate
I would be explicit:

The number is a decision support estimate, not an exact prediction

Here’s what would change the forecast

Here’s how we’ll track actuals vs. forecast

This builds trust and prevents misuse of the number.

Why this approach works
It balances:

Speed (tight timeline)

Rigor (validation + scenarios)

Transparency (assumptions and uncertainty)

Most importantly, it ensures leadership understands both the insight and its limitations, enabling better decisions.

If you want, I can condense these into shorter versions for a form with character limits or make them more technical (e.g., specific modeling techniques).