In a notable career pivot, Dr. Anshul Gandhi, a 34-year-old Indian-origin professional who trained as a dentist, is now a machine learning engineer at Apple’s AI division in California. Gandhi’s journey reflects a growing trend: health professionals exploring nonclinical, technology-driven careers.
A graduate of a dental college in India in 2013, Gandhi began his professional life treating patients in a small private practice. However, he soon realized that while he valued healthcare, his curiosity leaned more toward technology and problem-solving. Drawing on earlier exposure to programming (C++, Java) from his school days, he gradually upskilled in data analytics and coding through online platforms and hands-on projects.
He later secured a data analysis role in India, building foundational technical skills, then moved to the U.S. in 2016 for a master’s degree in biomedical informatics, a field that bridges healthcare and computing. During this period, Gandhi developed an interest in natural language processing (NLP) and computational modeling, particularly in structuring and analyzing complex datasets.
Over the years, he held roles such as data scientist and machine learning engineer at companies like Dell before joining Apple in January 2025. His prior work also included developing AI/ML systems for cybersecurity and efficiency optimization, as mentioned in interviews.
In a conversation shared on Business Insider, Gandhi said that his non-traditional background was initially seen as a challenge but later became his strength—his understanding of healthcare helped him stand out in AI applications related to health, wellness, and biomedical data.
Gandhi’s trajectory demonstrates how professionals from healthcare can make a career change into AI and tech by leveraging transferable skills, reskilling through formal education, and building domain expertise.
Dr. Gandhi’s path is not isolated. Across the health sector, clinicians, dentists, nurses, and allied health professionals are increasingly transitioning into tech roles—especially in health informatics, data science, medical software, and digital health product development.
As per recent reports, a rising number of Indian healthcare professionals are pursuing master’s programs or certifications in analytics, bioinformatics, and computing before moving into technology companies, mirroring Gandhi’s blend of medical expertise and computational skills.
Key enablers for such transitions include:
Transferable skills: Clinical reasoning, domain knowledge in human biology, patient workflows, and healthcare systems provide a strong foundation for roles in health tech.
Bridging education: Many professionals enroll in supplemental programs, bootcamps, master’s degrees (e.g. biomedical informatics, data science) to fill gaps in programming, statistics, and system design.
Project portfolios: Building personal or volunteer projects—including analyzing clinical data, contributing to open-source health tools, or developing small apps—can show credibility to employers.
Network leverage: Collaborating with technologists, attending hackathons or interdisciplinary conferences, and gaining referrals help in entering competitive roles.
A practical four-step guide for healthcare professionals making this shift suggests: (1) selecting a target tech domain, (2) assessing required skills, (3) enrolling in tailored training programs, and (4) launching a portfolio or internship.
Such transitions can offer a broader impact beyond patient-level care, providing opportunities to shape systems, tools, workflows, and policy at scale. In Gandhi’s case, his work exemplifies how interdisciplinary professionals can contribute to building AI systems that enhance user experiences and improve efficiency across products. Tech roles may also offer more flexibility, variety, and long-term growth in fields that intersect medicine and computation.
However, challenges are real:
Skill gaps: Many healthcare professionals lack prior coding or software engineering experience. Intensive learning and persistence are needed.
Credibility barriers: Hiring teams may prefer candidates with formal tech credentials or proven experience.
Role adaptation: Professionals must adapt from clinical decision-making to product thinking, system design, algorithms, and user experience considerations.
Identity shift: Moving away from a clinical identity (doctor, dentist, nurse) may involve emotional, cultural, or peer challenges.
Gandhi’s story stands as a reminder that a career pivot need not mean abandoning one’s roots. His journey—from handling dental instruments to developing neural networks—embodies the future of interdisciplinary expertise where medicine meets machine learning.
(Rh/Eth/TL/MSM)