Fullscript

Financial Planning and Analysis

• Revenue Planning, Forecasting & Model Ownership
• Manage the end-to-end revenue plan and rolling forecast. Own the annual budget and rolling forecast cadence, run scenario modeling, and update forecasts based on business performance and external factors.
• Own the revenue model and unit economics. Maintain a driver-based P&L and cohort/unit-level model that ties product, channel and customer behavior to financial outcomes.
• Run scenario and sensitivity analysis. Produce multi-scenario models, quantify upside/downside to key levers, and define economic guardrails and decision triggers.
• Be accountable for forecast accuracy and narrative. Own forecast accuracy targets, explain what changed/why/what to do, and drive continuous improvement in prediction quality.
• Lead the accountability cadence. Run the monthly revenue business review, present recommended corrective actions with named owners and timelines, and escalate proactively when thresholds or risks require intervention.
• Operationalize the model. Productionize templates and dashboards, partner closely with the Data and Strategic Finance teams to automate data pulls, validate assumptions, pressure test results, and ensure the model is decision-ready and scalable
• Reporting & Insights
• Deliver executive-grade reporting. Produce regular revenue reporting that includes performance vs. plan, leading indicators and cohort behavior, with driver-based variance explanations rather than surface-level summaries.
• Produce decision-oriented presentation materials. Deliver concise briefs and memos that surface quantified options, recommended actions, named owners and clear success metrics to enable rapid, well-informed decisions.
• Ensure traceability and rigor. Make sure every report and insight is directly traceable to model logic and assumptions.
• Close the loop on outcomes. Track follow-through on recommended actions, measure realized vs. expected ROI, and publish learnings that improve future forecasting and prioritization.
• Strategic & Cross-Functional Partnership
• Align revenue to strategy and roadmap. Partner with Strategic Finance and Product teams to translate roadmap and strategic pillars into explicit revenue assumptions and forecast implications.
• Translate GTM into forecast inputs. Work with Sales, Marketing and Commercial teams to convert pipeline, conversion, pricing and promotional dynamics into rigorous forecast inputs and scenarios.
• Help the business understand how growth happens. Move the conversation from “how much” to “how” — make growth drivers explicit, measurable and actionable.

Financial Planning & Analysis Manager

• Own the economic model. Become the owner of the segment financial and unit economic model, driving forecasting and tracking of the business
• Lead performance reporting. Produce clear performance narratives with insights, options, and recommendations
• Own the scorecard and diagnostics. Define and measure key business performance metrics and identify opportunities to improve outcomes
• Lead driver-based analyses to uncover performance drivers and areas for value creation
• Build investment-grade business cases, including scenario analysis, payback, and risk assessment
• Partner closely with the GM and leaders across Commercial, Product, and Operations to support decision-making
• Provide financial insight to guide tradeoffs across initiatives, investments, and operational decisions
• Develop a deep understanding of the business’s economic drivers and how they impact growth and profitability
• Maintain a dynamic economic model and contribute to the ongoing performance narrative
• Partner with data and analytics teams to operationalize insights into dashboards and tools

Strategic Finance Manager, Segment Lead

Machine Learning Engineer

• Lead the design, development, and deployment of production, multi-turn LLM-powered features, including summarization tools and clinician-facing conversational agents that support follow-up questions and reasoning over clinical context
• Own backend services in Python that integrate LLM agents with Fullscript’s platform and support reliable production use
• Help define technical direction for prompting, grounding, safety, and orchestration strategies used across clinical AI workflows
• Establish and improve evaluation approaches for LLM outputs, including accuracy, hallucinations, edge cases, and overall feature quality
• Shape engineering patterns for model-related workflows, including testing, CI/CD, observability, and version control
• Partner with medical, product, and engineering teams to identify high-value opportunities for AI and turn them into practical, scalable product capabilities
• Work cross-functionally with engineering, analytics, and medical SMEs to refine requirements and ensure data and system design support clinical use cases
• Provide technical leadership across projects by creating clarity in ambiguous problem spaces, guiding tradeoff decisions, and raising the quality bar for the team
• Stay current with the latest LLM research and emerging AI technologies, and help assess where they can be applied effectively at Fullscript

Staff Machine Learning Engineer

Product Manager

• Support strategy and delivery for high-impact product initiatives focused on improving care delivery and patient outcomes.
• Translate practitioner needs into scalable product opportunities, collaborating closely with Product, Design, Engineering, and Data.
• Apply technical fluency in backend systems, ML, and data architecture to inform product decisions and system design.
• Support the development and validation of AI/ML models, helping ensure medical accuracy, safety, and utility.
• Help evolve our clinical data infrastructure to support research, personalization, and feature development.
• Serve as a trusted voice in strategic product conversations: bringing clinical context, clear judgment, and a focus on outcomes.
• Collaborate with Clinical Research, Marketing, and Product teams to ensure our features are both credible and clearly communicated.

Senior Medical Product Manager

Backend Engineer

• Plan out solution designs and release strategies in collaboration with other engineering teams and stakeholders
• Implement the core business logic of our patient workflows to deliver a high quality, care focused experience.
• Build and maintain our GraphQL layer
• Solve performance issues
• Contribute to technical improvements, develop and maintain quality standards
• Code review and mentorship

Senior Developer, Backend – Patient Experience

Manager

• Lead and develop a team of technical writers and documentation specialists
• Own documentation strategy, governance, and lifecycle management
• Oversee publishing workflows and release documentation processes
• Administer and optimize our Content Management System
• Improve information architecture, taxonomy, tagging, and navigation structures
• Use analytics to improve search success, article effectiveness, and self-service performance
• Partner with Product, Engineering, Sales, and Support teams to align documentation with product evolution
• Drive continuous improvement in knowledge management practices
• Strengthen documentation processes that support AI-enabled support experiences

Manager, Support Documentation

Data Scientist

• Designing rigorous observational research frameworks that move beyond correlation toward defensible causal inference
• Addressing confounding, selection bias, missingness, and other structural challenges in real-world healthcare data
• Extracting outcome intelligence from newly acquired longitudinal practitioner datasets
• Connecting prescribing patterns, lab data, and behavioral signals into coherent analytical narratives
• Establishing methodological standards that protect the integrity of our claims
• Translating complex statistical findings into clear implications for Product, Marketing, and executive stakeholders
• Leading and mentoring a small data team while remaining deeply hands-on in research and modeling
• Building repeatable research processes that can scale as new data flows in

Lead Data Scientist

• Execute on M&A and strategic transaction processes, including target evaluation, diligence coordination, valuation, and deal structuring.
• Build and own detailed financial models (3-statement, cash flow, LBO-style, accretion/dilution, and scenario analyses) to evaluate potential transactions.
• Support deal negotiations by analyzing terms, sensitivities, and value drivers.
• Coordinate cross-functional diligence efforts across finance, legal, product, operations, and technology.
• Develop high-quality, defensible valuation analyses and underwriting materials.
• Assess synergies, risks, and integration considerations and translate them into actionable insights.
• Perform market sizing, competitive analysis, and benchmarking to support investment theses.
• Stress-test assumptions and clearly articulate key risks and upside scenarios.
• Create clear, compelling PowerPoint presentations for executive leadership and Board-level discussions.
• Translate complex financial and strategic analyses into concise narratives and recommendations.
• Prepare investment memos, diligence summaries, and post-close performance tracking materials.
• Support the identification and prioritization of acquisition, partnership, and build-vs-buy opportunities.
• Conduct industry, market, and competitive research within healthcare, wellness, and technology-enabled services.
• Track relevant market trends, regulatory developments, and capital markets activity.
• Partner with internal stakeholders to support integration planning and execution.
• Help track synergy realization and post-acquisition performance versus underwriting cases.