Scientific Engagement

Ataraxis AI Inc.

Job Overview

Location

Remote

Job Type

Full-time

Full Job Description

📋 Description

• Develop high-volume, high-quality scientific outreach to top physicians in the country, running scientific conversations at a peer level and converting them into business objectives.
• Present validation data, methodology, and clinical utility evidence fluently, handling hard questions on study design, comparators, and guidelines.
• Convert interest into first orders or early access participation, passing larger accounts to field sales with full context.
• Feed physician objections and evidence gaps back to clinical and product teams.
• Support field reps as the on-call scientific closer for their toughest accounts.

🎯 Requirements

• PhD (or MD/PharmD) in a relevant field, ideally related to cancer biology.
• Genuine command of clinical oncology literature; ability to read and critique trial design.
• Persuasive writer and speaker: You enjoy convincing smart, skeptical people. You can present in a scientifically charming way.

🏖️ Benefits

• Opportunity to shape the future of the company and the future of healthcare.
• Flat organizational structure, where every team member is empowered to actively contribute.
• Leadership roles are earned by those who demonstrate initiative and consistently deliver exceptional results.
• Strong work ethic and the ability to prioritize ruthlessly are essential.

Skills & Technologies

Remote

Degree Required

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Ataraxis AI Inc.

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About Ataraxis AI Inc.

Ataraxis AI provides AI-powered decision-support solutions for the healthcare industry. Its cloud platform ingests clinical, financial and operational data to predict patient deterioration, optimize staffing and reduce administrative burden. Targeting hospitals and health systems, the company delivers real-time dashboards that rank patients by risk, suggest interventions and quantify financial impact. Ataraxis AI integrates with existing electronic health records and claims feeds, offering explainable models built on de-identified datasets to support clinicians and administrators in improving outcomes and lowering costs.

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