Job Description

The client's current peak-sales forecasting framework produces strong numerical outputs and narratives, but requires

real-world forecast accountability

-- the kind held by people who've owned forecasts that drove BD, portfolio, or investment decisions.


We are looking for a senior commercial / forecasting expert to:

Write

"golden" peak-sales forecasts

for representative drug programs and standard prompts. Define

structural checks, scenario logic, and sanity bands

for automated forecast evaluations. Make explicit the

heuristics and base-rate assumptions

used by experienced forecasters to tell a realistic model from a speculative one.

Profile:

Industry Commercial Forecaster:

Director/Sr. Director/VP-level experience in

global forecasting, brand planning, or commercial insights

. Built and defended

patient-based peak-sales models

used in portfolio, BD, or investment contexts. Familiar with

forecasting for multiple drugs or indications

, particularly during pre-launch and early commercialization stages. Can articulate the reasoning behind

base-case assumptions

(penetration, price, ramp, LOE) and how they evolve post-launch. Has written or reviewed

governance-ready peak-sales models

(e.g., for launch committees or investor boards).

Market/VC/Buy-side Analyst:

Senior biotech equity analyst, VC incubation / BD lead, or company creation expert (e.g., from Third Rock, ARCH, Versant, RTW, Venrock, or similar). Built patient-level and revenue models used for

investment diligence

or

asset valuation

. Can critique or improve bottoms-up forecasts from an investor's perspective, identifying optimistic biases and false comparables.

Experience level

~10-15 years in biotech/pharma forecasting, investment, or commercial strategy roles. Experience spanning

pre-launch forecasts post-launch actuals

for multiple assets. CV/LinkedIn bullets like "led global forecast for [drug]," "responsible for long-range revenue planning and peak-sales scenarios," or "built patient-based forecasts for portfolio decisions." Strong comfort with

market modeling logic

(TPP inputs eligible pool penetration price/net ramp + LOE). Evidence of post-hoc learning -- can articulate where real-world results diverged from base-case assumptions.

Expectations:

Inputs we give:

Forecast prompts (representative TPPs, analogs, and SoC/pricing/launch assumptions). Access to anonymized or simulated data sets for building base cases.

Expected outputs (per prompt):

Golden Forecast Output:

A benchmark-quality peak-sales forecast (peak value, revenue curve by key years) plus a concise narrative (3-5 key drivers, 2-3 downside risks). The output should show how the expert calibrates realistic vs. inflated scenarios.

Forecast Rubric:

A structured evaluation framework with critical checks (market structure realism, patient flow logic, analog consistency, regional splits, LOE handling). Should define clear scoring thresholds -- e.g., unacceptable excellent*.

Know-how Layer:

Commentary explaining how experienced forecasters anchor their assumptions:

+ How they select base rates and analogs.
+ How they temper over-optimism (payer pushback, access limits, share ceilings).
+ How they identify when a model's structure or magnitude is implausible.

Engagement Model & Compensation

*

Contract / Part-time (Remote)

-- work flexibly with data science and evaluation teams.