The clients current peak-sales forecasting framework produces strong numerical outputs and narratives but requires real-world forecast accountability the kind held by people whove 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 investors perspective identifying optimistic biases and false comparables.
Experience level
-
1015 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:
Expected outputs (per prompt):
-
Golden Forecast Output: A benchmark-quality peak-sales forecast (peak value revenue curve by key years) plus a concise narrative (35 key drivers 23 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 models structure or magnitude is implausible.
Engagement Model & Compensation
- Contract / Part-time (Remote) work flexibly with data science and evaluation teams.
The clients current peak-sales forecasting framework produces strong numerical outputs and narratives but requires real-world forecast accountability the kind held by people whove owned forecasts that drove BD portfolio or investment decisions. We are looking for a senior commercial / forecasting e...
The clients current peak-sales forecasting framework produces strong numerical outputs and narratives but requires real-world forecast accountability the kind held by people whove 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 investors perspective identifying optimistic biases and false comparables.
Experience level
-
1015 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:
Expected outputs (per prompt):
-
Golden Forecast Output: A benchmark-quality peak-sales forecast (peak value revenue curve by key years) plus a concise narrative (35 key drivers 23 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 models structure or magnitude is implausible.
Engagement Model & Compensation
- Contract / Part-time (Remote) work flexibly with data science and evaluation teams.
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