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 forecastsfor representative drug programs and standard prompts. - Definestructural checks, scenario logic, and sanity bandsfor automated forecast evaluations. - Make explicit theheuristics and base-rate assumptionsused by experienced forecasters to tell a realistic model from a speculative one. ###Profile :Industry Commercial Forecaster :Director / Sr. Director / VP-level experience inglobal forecasting, brand planning, or commercial insights. - Built and defendedpatient-based peak-sales modelsused in portfolio, BD, or investment contexts. - Familiar withforecasting for multiple drugs or indications, particularly during pre-launch and early commercialization stages. - Can articulate the reasoning behindbase-case assumptions(penetration, price, ramp, LOE) and how they evolve post-launch. - Has written or reviewedgovernance-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 forinvestment diligenceorasset 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 spanningpre-launch forecasts → post-launch actualsfor 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 withmarket 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.