Building products that actually matter.

I’m a data driven product leader with three successful startup exits across multiple industries. I have deep experience in healthcare spanning payer, provider, patient, and pharma. I turn complex clinical and operational problems into elegant, scalable solutions.

Where I Thrive

Healthtech AI/ML Products Data Products 0β†’1 Launches Platform Products Enterprise B2B B2B2C Healthcare Interoperability
Jess Poeske

Jess Poeske

Product Management

πŸ“ Pittsburgh, PA

15
Years building products at startups
10+
Years in healthcare product development
3
Startup exits (including one $1B+ valuation)
0β†’1
Full lifecycle launches from concept to commercial scale
⬇ Download CV

A career spent building products across industries.

From early-stage startups to scaled enterprise platforms, each experience sharpened how I approach ambiguity, complexity, and user-centered decision making.

πŸ₯
Health-Tech
Payer, provider & pharma products; clinical data platforms
🌱
SDoH
Social determinants of health; care coordination & navigation
πŸ“‘
Smart Devices
IoT-enabled medical and operational hardware products
🚚
Transportation Logistics
Final mile, LTL, fleet management & dispatch optimization
πŸ’¬
Mobile Messaging
Scaled telecom messaging, alerts, and secure communication
πŸ’³
Fin-Tech
Share of wallet insghts, copertive financial census; 340B drug pricing compliance

How I Work

Process-driven, iterative product management.

I start with data to uncover real problems, move quickly through validation and delivery, and continuously loop learnings back into the system.
Click any step to explore.

01
Ideate
Product
02
Evaluate
Product
03
Prototype
Product + UX
04
Build
Eng + QA
05
Beta
Eng + Product
06
GA Release
Eng + Product
07
Track
Product
Goal
Generate ideas from: market direction, sales gaps, company strategy, user feedback, executive sponsor input, usage metrics, and cross-functional workshops.
β†’ Key Outcome: List of raw features with potential benefit
Goal
Discovery and research with internal and external stakeholders to understand use case, revenue potential, ROI, users, and the core problem being solved.
β†’ Key Outcome: Prioritized roadmap, defined features with KPIs/goals, high-level acceptance criteria, and scope
Goal
Carry out user testing and elicit feedback with anything from high-level wireframes to fully clickable prototypes in Figma, Balsamiq, or PowerPoint.
β†’ Key Outcome: Updated features, roadmap, and KPIs based on testing and feedback
Goal
Feature development, QA, UAT, and iterative updates based on QA and UAT findings. Engineering and QA own delivery quality.
β†’ Key Outcome: Technical readiness for a beta or GA feature release
Goal
Establish beta release scope, target users, KPIs, and goals. Deploy beta release and actively track performance against targets.
β†’ Key Outcome: Insights, fixes, and improved readiness for broader GA release
Goal
Define and execute release readiness plans including training, communications, sales impact, demos, marketing materials, support plans, and knowledge base articles.
β†’ Key Outcome: Deploy GA release with all supporting materials and plans delivered
Goal
Track the feature's main goals and KPIs to see if it's performing as expected, and diagnose why or why not with data and user feedback.
β†’ Key Outcome: Establish fixes or feature improvements based on feedback and analytics β€” feeding back into Ideate

Prioritization Frameworks

There are LOTS of great frameworks β€” but there isn't a one-size-fits-all framework. The right choice depends on product maturity, scale, business type, and the specific context you're operating in. And no framework is a silver bullet that will solve every problem!

PRICE: An adapted framework PRICE

The PRICE framework is my adaptation of Intercom's RICE framework, modified to better fit enterprise and B2B products. This is how I adapt frameworks to the business context rather than applying them blindly.

PRICE Formula

(P Γ— R Γ— I Γ— C) Γ· E

P

Potential β€” How potentially repeatable is this feature/use case for other customers?

Massive = 3  Β·  High = 2  Β·  Medium = 1  Β·  Low = 0.5  Β·  Minimal = 0.25

R

Revenue β€” How large of an annual revenue/value generator is this?

Massive = 9–10  Β·  High = 7–8  Β·  Medium = 5–6  Β·  Low = 3–4  Β·  Minimal = 1–2

I

Impact β€” How much will this impact each person?

Massive = 3  Β·  High = 2  Β·  Medium = 1  Β·  Low = 0.5  Β·  Minimal = 0.25

C

Confidence β€” How confident are you in your estimates?

High = 100%  Β·  Medium = 80%  Β·  Low = 50%

E

Effort β€” How many person-months will this take?

Use whole numbers; minimum 0.5 months

Sample PRICE Scoring

FeatureFeat. P R I C E ScoreS
Centralized HIPAA LoggingHIPAA Logging33390%124.3
Governor 2.0Governor 2.026290%121.6
Data Quality EnforcementData Quality34290%121.6
Multi-layer PDF SupportPDF Support26280%119.2
Auto-detect Scanned ContentScanned Content26280%119.2
API Performance ImprovementsAPI Performance37370%314.7

Roadmap Execution

A real roadmap snapshot from Alexandria Charts, organized into thematic waves that balance customer value, security, and engineering health.

Now
"Under-the-Hood" Quality
Q3 2022
  • Bulk Extraction 2.0
  • Clearsense implementation
  • Text extraction + conversion improvements
  • Enforce data quality proactively
  • Auto-detect scanned content
  • Native multi-layer PDF support ✱
  • HIPAA breach testing
Next
Data Access + Security
Q4 2022
  • Governor 2.0
  • Expand OAuth support + consistency
  • Deprecate Basic Auth/HMAC
  • Centralized HIPAA logging
  • OAuth scope consistency (Indexing)
  • Azure AD migration
  • Images migration
Later
Customer Controls
H1 2023+
  • Improved API consumer controls
  • SLA dashboards
  • Audit Tool 1.0
  • API performance improvements (cont.)
  • Indexing improvements
  • Disaster Recovery testing
  • AWS WAF to IRT and OpUI

✱ Stretch goal

Case Studies

A selection of product work spanning 0β†’1 commercial launches, clinical AI strategy, and market strategy. These reflect the full breadth of my product leadership.

πŸ“Š
Full Portfolio β€” PowerPoint Version Includes original mockup screenshots, brand assets, roadmaps, and more!
View in Google Slides β†—

Commercializing UPMC's unstructured clinical data platform

ProblemCommercialize UPMC intellectual property into a standalone healthcare AI company

Alexandria Charts is a foundational case study that demonstrates how I build products from 0β†’1. This project spans strategy, discovery, execution, and scale, reflecting the full breadth of product leadership.

View the Full Case Study β†—
  • 🎯
    Create: Mission + Value PropositionDevelopers get a robust data platform and APIs to unlock the 50-80% of patient data captured in unstructured clinical notes.
  • πŸ”
    Identify: Target MarketEarly to mid-stage healthtech companies solving problems with AI, ML, NLP, and unstructured data. Not health systems directly.
  • πŸ“Š
    Build: Business Model + Market Sizing$375B combined TAM in 2030 across AI/ML/NLP and data analytics segments.
  • πŸ—οΈ
    Generate: Business Plan + BrandBuilt independent business plan, brand identity, and go-to-market assets from scratch.

Platform Impact

Since 2015, Health Fidelity used Alexandria Charts to build their Lumanent Retrospective Review tool for UPMC Health Plan's Medicare Advantage risk adjustment team. The company was acquired by Edifecs in December 2021.

$XXXm+ Revenue generated for a single customer using the platform

What the platform does

  1. AggregatePull and consolidate unstructured clinical notes from multiple source systems
  2. NormalizeStandardize and reconcile patient identities across disparate data sources
  3. GovernControl, audit, and enforce data access policies via OAuth and HIPAA-compliant logging
  4. EnhanceEnrich data with OCR, Generative AI, and NLP to unlock the full value of unstructured content

Nursing AI Virtual Assistant Strategy

ProblemExpand existing clinical documentation products into a new adjacent healthcare market

Nurses spend 2-4 hours per 12-hour shift entering 631-875 data points manually. 85% of nursing documentation is discrete data entry. I identified three AI product tiers to address this burden.

View the Full Case Study β†—
  • 🎀
    Scribe TierAutomatically capture and record assessments in the chart via voice.
  • πŸ€–
    Agent TierSpeed up assessment capture with templates, surface appropriate nursing care plans, create automated shift summaries.
  • πŸ’‘
    Advisor TierAsk for or navigate to relevant EMR information, create reminders, communicate via secure chat integration, voice-enabled chart entry.

Strategic Recommendations

Three investment paths with distinct return profiles:

  • Quick Win: Enterprise Triage Nurses Use current solutions with no changes. Educate sales team on use case and highlight enterprise deal value.
  • Bundle into Fluency Direct Contracts Reduce implementation cost, simplify user creation, adjust base pricing to create accessible entry point.
  • Data Collection for Future AI Products Collect training data for CAPD, Auto Assessments, and Ambient Physician Documentation.

Redesigning Data Privacy & CBO Network Architecture

ProblemBuild technology to support community based organizations serving vulnerable populations

Healthify's referral network connected patients to community-based organizations for social needs β€” but the data sharing model created legal, ethical, and CBO recruitment challenges. This project revamped the permissions architecture to make data handling more transparent, compliant, and client-centered.

View the Full Case Study β†—
  • πŸ”’
    Privacy ArchitectureRevamped the permissions model so client data is shared in accordance with legal requirements and ethical standards β€” eliminating the "gotcha" moment where customers were surprised by data sharing scope at implementation.
  • 🏘️
    CBO Network GrowthSome CBOs had opted out of the platform entirely due to data sharing concerns. Improved privacy architecture unlocks those organizations and expands network recruitment potential.
  • πŸ‘€
    Client-Centered ReferralsRedesigned the referral model to put client needs first β€” reducing the burden on vulnerable individuals to navigate between referral senders and recipients.
  • βš–οΈ
    Compliance + TrustAddressed both legal obligations and ethical responsibilities around client data, increasing user and customer confidence in the Healthify platform and participation model.

Why Build It

  • Ethical obligation The clients Healthify serves are vulnerable. Their data deserves to be handled responsibly and with appropriate consent.
  • Legal obligation Platform data sharing practices needed to align with legal requirements governing how client data is shared across organizations.
  • CBO recruitment CBOs that previously opted out due to data concerns become recruitable with a stronger privacy model.
  • Customer confidence Eliminated the implementation reveal β€” where customers discovered just how much data was shared in the network β€” which not infrequently went poorly.
Healthify CBO assignment architecture diagram

Final Mile Platform Portfolio Strategy & Roadmap

ProblemDefine a product portfolio strategy for last mile logistics across pickup and delivery

Maven Machines had a growing set of logistics products spanning two distinct markets. This project defined a cohesive portfolio strategy across LTL and Final Mile delivery, balancing competing resource demands and sequencing investments to seize market opportunity.

View the Full Case Study β†—
  • πŸ—ΊοΈ
    Portfolio ArchitectureDefined product strategy across two markets: LTL (Dispatch, Outbound/Linehaul/Inbound Planning) and Final Mile (Route Optimization, Final Mile Planning), with shared supporting mobile apps.
  • πŸ“Š
    Roadmap PrioritizationBalanced competing resource demands across a multi-product portfolio β€” sequencing from Sales Ready through In Progress using structured prioritization, market research, and stakeholder alignment.
  • πŸš€
    Market ExpansionIdentified Final Mile as an adjacent growth opportunity from the established LTL base, with API-first route optimization as the strategic bridge between the two markets.
  • πŸ“±
    Supporting ProductsCoordinated the Driver App and Dock Worker App across both market segments to ensure cohesive, end-to-end platform coverage without duplicating engineering effort.

Platform Scope

Two-market product portfolio:

  • LTL Market Dispatch Β· Outbound Planning Β· Linehaul Planning Β· Inbound Planning
  • Final Mile Market API Route Optimization Β· Final Mile Planning
  • Supporting Apps Driver App (both markets) Β· Dock Worker App (LTL)
Maven Machines Final Mile Platform architecture diagram

Diagnosing & Reversing Premium Subscriber Decline

ProblemNet loss of premium subscribers over the last 12 months

Facing sustained premium subscriber decline, I led a structured analysis of conversion funnel changes across product versions. The investigation surfaced a strong correlation between specific workflow changes and negative conversion trends β€” providing a clear, evidence-backed path to reversing the decline.

View the Full Case Study β†—
  • πŸ“‰
    Root Cause AnalysisIdentified strong correlation between VVM changes and negative conversion trends β€” specifically the removal of "Upgrade to Premium" in Middlesex 7.x and the addition of 2 extra steps to complete purchase across Middlesex 7.x and Newport 8.x.
  • πŸ”
    Purchase Workflow OverhaulTop priority recommendation: simplify and improve the purchase workflow before any pricing changes. Agile implementation enables real-time measurement and iteration on each change.
  • πŸ’°
    Price Sensitivity TestingA/B test to optimize product offering mix. Apply pricing psychology principles to simplify choice and guide decision-making at the Choose Plan step.
  • πŸ§ͺ
    Evidence-First SequencingStrong evidence points to workflow friction β€” not price β€” as the primary driver of lost conversions. Fix the funnel first; test pricing second.

Key Findings

  • VVM Changes Drove Decline Correlation between specific version changes and the negative conversion trend was strong enough to prioritize workflow fixes above all else.
  • Friction Added at Critical Moment Removing the "Upgrade to Premium" CTA and adding 2 steps to purchase created unnecessary friction right at the conversion point.
  • Multiple Purchase Decisions β€” Newport 8.x Adding a purchase decision with multiple plans introduced choice overload, further suppressing conversions.
  • Pricing Changes Secondary A/B price testing is recommended, but only after workflow improvements are in place and measurable.

Rapid Prototyping with Generative AI

I use generative AI tools to rapidly prototype product ideas, gather early user feedback, and iterate quickly without significant UX or engineering investment. Some projects even include full CI/CD pipelines. Below are a few examples built around problems from my own life.

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Hi, I'm Jess!

Jess Poeske

I’ve spent my career building products in complex industries, helping early ideas grow into real companies. I’m especially drawn to healthcare challenges where better systems can improve patients’ lives while letting technology fade into the background.

Outside of product work, I’m active in Pittsburgh’s startup and arts communities and enjoy experimenting with new ideas and tools.

Product Philosophy

  • β†’

    Customer and data first. Every decision anchored in user evidence and measurable outcomes, not opinions.

  • β†’

    Frameworks are tools, not rules. The right framework depends on product maturity, business type, and context. No silver bullets.

  • β†’

    Complexity is not the enemy. Some problems are hard. I lean into that complexity and build teams and systems that thrive in it.

  • β†’

    Mission matters. I do my best work when the product I'm building has the potential to genuinely improve peoples' lives.

πŸ†
Recognition
Pittsburgh 30 Under 30 class of 2021
🎭
Passion
Opera singer + Pittsburgh Festival Opera alum
🌱
Hobbies
Gardening, running, audiobooks
πŸ“
Location
Pittsburgh, PA

Let's build something that matters.

Open to product leadership roles in healthtech, AI-driven platforms, and mission-driven organizations.