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.

Skills Required