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Aidoc

Information Delivery & Visualization Engineer

Tel Aviv-Yafo, Tel Aviv District, Israel

Location
Tel Aviv-Yafo, Tel Aviv District, Israel
Work
Hybrid
Employment
Not stated
Level
Not stated
Function
Engineering
Pay
Not stated
Posted
2 weeks ago

About the role

About Aidoc

Aidoc is the market leader in healthcare AI and has developed the world's largest clinical frontier model, supporting nearly 50 million patients each year.

Aidoc is deployed in more than 2,000 medical centers worldwide. Since our founding in 2016, Aidoc has raised over $500 million and achieved a record number of FDA-cleared solutions.

Medical diagnosis is hitting a breaking point, where the number of physicians hasn't kept up with the rapidly growing patient load. This leads to misdiagnosis and treatment delays that result in hundreds of thousands of deaths every year. Aidoc assists clinicians by precisely highlighting diseases directly on medical images, preventing misdiagnosis and ensuring that urgent patients receive the immediate attention they need.

About this role

Aidoc’s Enterprise Decision Intelligence (EDI) team is the company’s in-house strategy and analytics

function. We help leadership make better, faster decisions in two ways: deep-dive analyses of the

company’s biggest strategic questions (entering a new market, pricing, the ROI of a major initiative), and

the company-wide KPIs and “single source of truth” that everyone relies on. Our work is used by the C-

suite and the Board.

We’re looking for an Information Delivery & Visualization Engineer, a BI-minded builder, to be the face of

our data. Today, much of our reporting lives in manually built slide decks; you’ll lead the shift to something

live, polished, and trusted across the company.

This is a role with real ownership and visibility. You’ll set the standard for how data is presented at Aidoc,

work directly with senior stakeholders, and learn from a small, senior team of data engineering, data

science, and BI experts. The scope is intentionally broad, so you’ll grow across the data stack. As the

function evolves, there are several paths forward, and we’ll shape the direction with you.

Responsibilities

  • Dashboards & self-service analytics: Design, build, and maintain interactive dashboards ( Databricks) that make KPIs and deep-dive findings available to executives and teams across the company. This is bespoke work, not template charts: custom visuals, tailored layouts, and purpose- built interactive views (drill-downs, scenario toggles, and the like) designed around each audience’s decisions.
  • From slide decks to live insights: Upgrade and replace recurring manual KPI decks for the C- suite and Board with automated, always-up-to-date dashboards.
  • Enablement: Train and support stakeholders in Sales, Marketing, Finance, People, Clinical, and R&D to answer their own questions, and track adoption to keep improving. Own the foundations
  • KPI dictionary: Lead the creation and upkeep of the company’s KPI dictionary / data catalog : clear definitions, owners, and calculation logic, adopted across teams.
  • Data quality: Own improving our data integrity as Aidoc scales, holding our numbers to an enterprise-grade level of rigor, so people can trust the numbers.
  • New KPIs: From time to time, work with stakeholders to define new KPIs, together with our data engineer and data scientist.

Requirements

  • Over 5 years of experience in BI development, data visualization, analytics engineering, or a similar role.
  • Hands-on experience building dashboards in a modern BI tool (Databricks, Tableau, Power BI, Looker, or similar), including custom-built work that goes beyond default templates and standard chart types.
  • A strong eye for design: you turn complex data into clear visuals that executives can read in seconds.
  • Business understanding: the drive to ask why a KPI matters, not just how to chart it.
  • Clear communicator with both technical and executive audiences; full professional English.
  • Great collaboration skills: you build trust and partner easily with many teams across Aidoc (Sales, Marketing, Finance, People, Clinical, R&D, and more), and you gather requirements, align on definitions, and handle competing priorities with patience and good judgment.
  • Ownership mindset: a self-starter who is comfortable with ambiguity and building from scratch, under guidance.
  • Versatile and curious: you enjoy variety (building, documenting, cleaning data, working with stakeholders) and take pride in the behind-the-scenes work that makes data trustworthy. Nice to have:
  • Databricks experience.
  • Strong SQL, and a solid understanding of data modeling and data warehousing.
  • Working proficiency in Python for data manipulation.
  • Experience with ETL pipelines and a cloud data platform (AWS, Azure, or GCP).
  • Experience with SaaS or healthcare metrics (e.g., ARR, ACV, clinical KPIs).
  • Front-end or UX/UI design experience (e.g., custom visualization libraries such as D3, Plotly, or similar).
  • Basic statistics for KPI and metric development.

Working at Aidoc

Our Perks

  • Be part of something big - using cutting-edge technologies to transform the Healthcare industry (while saving patients’ lives)
  • We work in a hybrid model, with our new offices located at 34 HaMasger Street in Tel Aviv and parking for employees.
  • Amazing and healthy breakfasts and lunches prepared daily by our personal chef!
  • Stocked up kitchen & meal card
  • Wellness: Aidoc employees-only gym, plus Pilates, Yoga, and functional workouts classes.
  • Amazing culture - collaborative, transparent & fun!
  • Attractive compensation package & benefits

Aidoc is deeply committed to creating an inclusive workplace, and to the principle of equal opportunity for all individuals. We prohibit discrimination and harassment based on race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other status protected by law.

Source

Posted by Aidoc on their own hiring page, job-boards.eu.greenhouse.io. OptiTalent shows it as published, read 07 Nov 2026 01:26 UTC; the original is the reference.

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