Epistemix

Epistemix

Synthetic Population Engineer

US

No sponsorshipDetected 226 days ago
PythonPostgreSQLMachine LearningData ScienceStatisticsCustomer SuccessEpidemiology

About the role

  • The Synthetic Population Engineer uses cutting edge data science techniques to continuously improve the Epistemix synthetic population.
  • A synthetic population is a statistically representative model of a population of real people and their environment, including personal attributes, social connections, and associations between people and places.
  • Traditional analytical techniques built on historical data were not built for this.

Responsibilities

  • Create visualizations for marketing and productizing synthetic populations.
  • Develop innovative methods for supporting external users in augmenting Epistemix synthetic populations with their own proprietary data.
  • Support the synthetic populations team in engaging with customer success, professional services, and engineering teams to understand project specific synthetic population requirements.
  • We build simulation and data-driven modeling tools that let leaders visualize how strategies will unfold across populations and systems before they commit resources.
  • By clarifying which variables drive outcomes, where leverage exists, and how they interact, we help organizations move from uncertainty to conviction.
  • We are approaching our Series B and actively building the team that will define what comes next.

Requirements

  • Proficient experience in:
  • Having a startup mentality with understanding the risks and the ability to flex across needs of an evolving team in a fast-paced environment.
  • Using Python for data science applications.
  • Working with geospatial data.
  • Working with simulation or machine learning models.
  • Demonstrate empathy for users and decision makers by explaining how the synthetic population was created (e.g., which data sources and models were used) in an accessible way for all.
  • Possessing the passion to build the standard for synthetic populations globally to improve decision making across social, health, economic, and environmental policies and advancing data science into more commercial applications.
  • A PhD or master's degree in Data Science or a relevant technical discipline such as Computer Science, Mathematics, Statistics, Epidemiology, or Public Health.
  • Proven track record of success building data products and/or data marketplaces.
  • WHY JOIN EPISTEMIX?
  • By joining Epistemix, you will become part of a collaborative and rapidly growing team that values curiosity and creativity. We are fully remote, with team members in the United States and Europe. Benefits include:
  • Equity & Incentives - Participation in our stock option program.

Nice to have

  • Working with relational databases such as PostgreSQL (additional database management experience preferred).

Benefits

  • Health, Welfare and 401(k) Programs - Eligibility for benefits (for U.S. employees).
  • Together, these capabilities let decision-makers stress-test strategies in a controlled environment before deploying them in the real world across healthcare, consumer industries, insurance, and government.

Company info

  • These datasets empower customers to solve problems in domains where empirical data is unavailable due to legitimate concerns for personal privacy or where desired data simply does not exist.
  • The Synthetic Population Engineer contributes to profitable growth by making the synthetic population more useful and accessible to customers, increasing adoption and utilization.
  • Having a synthetic population that is continuously updated and improved over time is critical to building trust in the models and solutions built with our platform.

Visa & Work Authorization

  • Candidates must possess the legal right to work in their intended work location, as we are currently unable to sponsor or transfer employment visas for any country, including the United States

This listing is sourced directly from Epistemix's careers page and normalized into a canonical job model.