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Remote Peak Sales Forecasting Expert - AI Trainer ($200-$275 per hour)
Remote Peak Sales Forecasting Expert - AI Trainer ($200-$275 per hour)Mercor • Boise City, Idaho, US
Remote Peak Sales Forecasting Expert - AI Trainer ($200-$275 per hour)

Remote Peak Sales Forecasting Expert - AI Trainer ($200-$275 per hour)

Mercor • Boise City, Idaho, US
[job_card.variable_days_ago]
[job_preview.job_type]
  • [job_card.part_time]
  • [filters.remote]
[job_card.job_description]

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