Data Scientist at Imagen Technologies (Ats) (San Francisco, CA)

Imagen Technologies (Ats)· San Francisco, CA·3d ago

Role details

Work type

Onsite

Employment

Full-Time

Job description

Culture: Built In profile notes a mission-driven environment focused on eliminating errors in radiology. The team is described as an experienced mix of clinicians, technologists, and industry professionals.
Work Model: Hybrid workspace based in New York, NY. Employees engage in a combination of remote and on-site work with flexible on-site time.
Notable Perks:
- Offers company equity and an employee stock purchase plan
- Generous parental leave and family medical leave
- Health, dental, and vision insurance with FSA and commuter benefits
- 401(K) and disability insurance
- Generous PTO, paid holidays, and paid sick days
Engineering/Clinical Culture: A strong focus on using technology to augment, not replace, clinical expertise. Radiologists are offered flexible, remote work options with no call or commute requirements, suggesting a high degree of autonomy.

One-liner: Imagen Technologies combines clinical expertise with advanced AI and imaging technology to help healthcare organizations deliver faster, more accurate diagnostic care.
Entity Type: Private (Venture-backed; last known round: Series C)
Headquarters: New York, New York, United States
Founded: 2015
Founders: Alex Dresner (Co-Founder & CEO), Rob Lindsey, PhD (Co-Founder & Chief Artificial Intelligence Officer)

Primary industry: Healthcare Technology (Healthtech) / Diagnostic Imaging
Target customers: B2B – Healthcare organizations including hospitals, imaging centers, and physician practices
Mission or purpose statement: "We believe the right diagnosis should be available to every patient, at the right time." Imagen is building a new model for diagnostic imaging that combines clinical expertise, advanced technology, and infrastructure to deliver high-quality care at scale.

AI-Enabled Radiology Services: A full-service teleradiology platform that connects healthcare organizations to a network of over 200 subspecialist radiologists. The platform uses proprietary AI for quality tracking, workflow optimization, and 100% AI-enabled quality assurance, enabling a 17-minute average STAT turnaround time and a <1% addendum rate.
Imagen AI Software: State-of-the-art machine learning algorithms designed to assist radiologists in detecting abnormalities, reducing errors, and improving diagnostic accuracy across various imaging modalities.

Valuation/Market Cap: Not publicly disclosed
Key Metric: Total funding of $166.3M across 6 rounds. Notable rounds include a $75M Series C led by Casdin Capital (Feb 2021) and a $32M venture round (Nov 2024).
Notable Investors/Partners: Casdin Capital (lead Series C), alongside other venture investors.
Growth Signals: The company operates across 25 states and 200+ sites of care, interprets over 2 million studies annually, and employs 128 people (+22.6% YoY growth). It has a 90% annual radiologist retention rate and maintains >95% SLA adherence.

Integrated AI + Clinical Network: Unlike pure teleradiology or pure AI startups, Imagen combines a large network of subspecialist radiologists with proprietary AI to drive quality and speed.
Operational Scale & Reliability: Proven ability to handle high volume (2M+ studies/year) with consistent SLA performance and extremely low error rates (<1% addendum rate).
Radiologist-Centric Model: High retention (90%) suggests a strong value proposition for clinicians, including flexible scheduling and no-call options, which is a key differentiator in a talent-constrained market.
HITRUST Certified: Demonstrates a strong commitment to data security and compliance, a critical trust factor for healthcare clients.

Scaling Network & Technology: Expanding into new geographic markets and sites of care while deepening AI capabilities to improve diagnostic accuracy and workflow efficiency.
Addressing Radiologist Shortage: The company is positioned as a solution to the growing gap between imaging demand and radiologist supply, making its model increasingly relevant to health systems.

Sources