Truveta

Truveta

Partner Data Analyst - Life Science Partnerships

Seattle, WA

Sponsorship not specifiedDetected 23 days ago
PythonSQLData ScienceStatisticsProduct ManagementExcelCustomer SupportEHR/EMRClinical TrialsClinical ResearchResearchExperimental DesignBiostatisticsEpidemiology

About the role

  • Achieving Truveta's ambitious vision requires an incredible team of talented and inspired people with a special combination of health, software and big data experience who share our company values.
  • Truveta was born in the Pacific Northwest, but we have employees who live across the country.
  • Our team enjoys the flexibility of a hybrid model and working from anywhere.

Responsibilities

  • Build strong, trusted relationships with customer stakeholders, acting as a key partner in advancing their research goals
  • Apply appropriate research methodologies, including cohort design, bias mitigation, and statistical analysis
  • Communicate on the status of various projects and collaborate with success managers to ensure that Truveta clients can be successful with the platform.
  • Lead customer-facing discussions to unblock research challenges and drive projects forward
  • Deliver both rapid-response analyses and deeper, in-depth investigations depending on customer needs
  • Provide technical support and troubleshooting for customer analyses, code, and notebooks
  • Create enablement materials as the Truveta platform evolves to help onboard new researchers to our increasing capabilities.
  • Collaborate cross-functionally to investigate data quality and demonstrate data reliability
  • Deliver feedback to internal teams based on customer requests to inform Truveta's product roadmap.
  • Willingness to learn new coding languages including internal proprietary coding language to analyze data and build cohorts.

Requirements

  • Undergraduate or graduate degree in clinical research, epidemiology, biostatistics, data science, clinical informatics, or a related field
  • 5+ years of experience working in a technical customer-facing role, delivering high-quality research studies & analyses.
  • Strong proficiency in SQL, R and/or Python for statistical analysis
  • Strong understanding of real-world evidence principles, including bias, confounding, and study design considerations.

Nice to have

  • These qualifications are preferred but not required.
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Compensation

  • In person attendance is required at least once per year for an onsite meeting.

Benefits

  • Truveta is the world's first health provider led data platform with a vision of Saving Lives with Data.
  • Truveta is rapidly building a talented and diverse team to tackle complex health and technical challenges.
  • 3+ years of experience wrangling and analyzing Electronic Health Record data or other Real World Data sources in life science companies, CROs, other EHR data companies or healthcare organizations.
  • Experience working across large, multi-domain healthcare datasets with millions of patients and hundreds of millions of records, leveraging distributed computing frameworks for scalable analysis.

Company info

  • Partner closely with life sciences customers to understand research questions and translate them into analytical approaches
  • Guide customers on best practices in real-world data research, including study design tradeoffs and interpretation of results
  • Collaborate with customers to support peer-reviewed publications and high-quality research outputs
  • Experience delivering high-quality product demonstrations that enable customers or stakeholders to understand and effectively leverage data, platform capabilities, and analytical workflows.
  • Our mission is to enable researchers to find cures faster, empower every clinician to be an expert, and help families make the most informed decisions about their care.
  • Beyond core capabilities, we are seeking problem solvers, passionate and collaborative teammates, and those willing to roll up their sleeves while making a difference.

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