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 & CompensationContract / Part-time (Remote) — work flexibly with data science and evaluation teams.