Peak Sales Forecasting Expert

Mercor; Berlin; Vor Ort; EUR 40.000 - 60.000; Teilzeit

Zusammenfassung

A global forecasting firm in Berlin is seeking a Senior Commercial / Forecasting Expert to produce high-quality peak-sales forecasts for drug programs. The ideal candidate has 10–15 years of experience in biotech or pharmaceutical forecasting, with a strong ability to define market models and articulate assumptions. This part-time, remote position offers flexible engagement with data science teams.

Qualifikationen

• 10–15 years of experience in biotech/pharma forecasting or commercial strategy roles.
• Experience with pre-launch forecasts and post-launch actuals for multiple assets.
• Ability to critique and improve bottom-up forecasts.

Aufgaben

• Write peak-sales forecasts and standard prompts.
• Define structural checks and scenario logic.
• Build benchmark-quality peak-sales forecasts.

Kenntnisse

Forecast modeling expertise

Critical thinking

Biotech knowledge

Analytical skills

Jobbeschreibung

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

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.