Peak Sales Forecasting Expert

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.