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Peak Sales Forecasting Expert
Mercor
- London, England, United Kingdom
- London, England, United Kingdom
About
at
Mercor
Overview 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.
Responsibilities
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 distinguish a realistic model from a speculative one.
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.
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.
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Languages
- English
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