How AI is redefining talent and innovation in life sciences

As in other fields, AI has become essential in life sciences, accelerating advances in healthcare, pharmaceuticals, genomics, and biotechnology. To put things into perspective, the global AI market for pharmaceuticals is expected to reach $16.49 billion by 2034, while AI in medical devices is expected to grow to $97.1 billion by 2028, driven by the proliferation of AI-powered diagnostic systems, smart wearables, and surgical robots.

In this rapidly changing landscape, AI professionals are becoming the most sought-after talent by life sciences organizations. These experts are redefining how diseases are diagnosed, drugs are discovered, and care is delivered. This is both an opportunity and an inflection point for the Indian life sciences ecosystem. The convergence of biology, data, and machine learning is opening up entirely new career frontiers. That said, it is essential for existing workforces and aspiring candidates in this sector to understand how the work landscape is likely to evolve and the challenges this may pose.

The rise of the role of AI-driven healthcare and drug discovery

AI specialists are at the core of this transformation. From building predictive diagnostic models to automating clinical workflows and enabling real-time patient monitoring, their expertise supports innovation across the healthcare value chain.

Pharmaceutical companies are increasingly hiring AI researchers, machine learning engineers, computational biologists, and bioinformatics experts to accelerate drug discovery and enhance clinical development. These experts design algorithms that predict molecular behavior, evaluate drug efficacy, and shorten research and development timelines. Activities that previously took years now proceed within months.

At the same time, digital health and biotech startups are leveraging AI to design remote care solutions, develop imaging software, and analyze large amounts of health data. In these setups, AI engineers and data scientists collaborate with clinicians to improve diagnostic accuracy and create more personalized treatment options.

Demand grows across pharmaceuticals, biotech and health technology

Life science companies and contract research organizations (CROs) in India are significantly expanding their adoption of AI. Bengaluru, Hyderabad, Pune, Mumbai and Delhi-NCR have emerged as major employment hubs for AI professionals in this sector.

Some of the main application ranges are listed below.

• Pharmaceuticals and biotechnology: AI accelerates target identification, molecular design, and prediction of drug-target interactions. Enhance efficacy and toxicity assessments and advance personalized medicine through integrated genetic and clinical data.
• Clinical trials: Algorithms streamline patient recruitment, site selection, and trial management, increasing efficiency and reducing costs.
• Manufacturing and supply chain: Predictive Analytics improves maintenance planning, regulatory compliance, and demand forecasting.
• Medical technology: AI powers device simulation, imaging, predictive maintenance, and generative design of patient-specific medical devices, creating entirely new revenue streams.
• Academic medical center: From immersive learning tools to automated research management, AI is modernizing the way medical research and education is conducted.

These use cases highlight a new paradigm in which AI is central to value creation across the life sciences ecosystem. In this context, the increasing integration of AI across research, clinical development, and operations is rapidly increasing the demand for professionals who can ensure that innovations translate into tangible scientific and business outcomes.

Expanding the scope of AI roles

AI expertise in life sciences is no longer limited to data science or research labs. The organization is hiring for a wide range of roles that blend deep technical skills with scientific insight. Compensation reflects both expertise and seniority. For roles like bioinformatics scientist, ML engineer, data scientist, etc., entry-level professionals typically earn between Rs 300,000 and Rs 12,000,000 (LPA) per annum, depending on the role and specialty. The salaries of mid-level experts, i.e. those with 3 to 7 years of experience, range from LPA 8 Rs to 30 Rs, while the salaries of senior experts and research leaders range from LPA 18 Rs to LPA 60 Rs and above.

Key roles driving this demand include scientists and engineers in bioinformatics (LPA 3 to 35 rupees), machine learning engineers in life sciences, imaging, or genomics (LPA 6 to 50 rupees), data scientists in clinical or pharmacovigilance areas (LPA 5 to 40 rupees), and AI research scientists focused on drug discovery or computational biology (LPA 12 to 40 rupees). 60LPA). Other high value positions such as Computational Chemist, Structural Biology Engineer, MLOps or AI Implementation Engineer, and Clinical AI Manager typically fall within the LPA range of Rs 12 to 50 with higher leadership roles.

These numbers highlight how interdisciplinary expertise – combining AI proficiency with biological or clinical acumen – can help you build a solid career in the life sciences. Senior professionals who can lead teams at the intersection of AI, drug development, and regulatory science are earning high compensation as the industry matures. Now, the rewards are impressive, but climbing the ladder in this segment requires a certain specific skill set.

Skills that define the new age of AI professionals

Employers are now prioritizing candidates with interdisciplinary expertise – candidates who can blend computational skills with biological understanding. Some of the most sought-after technical competencies include:

• Machine learning and deep learning
• Bioinformatics and computational chemistry
• Natural language processing (NLP) for medical text analysis
• Computer vision for imaging and diagnostics
• Machine learning operations (MLOps) for scaling AI models in production

Similarly, skills in the areas of genomics, proteomics, pharmacovigilance, and clinical data analysis are equally valuable. An added benefit is understanding regulatory frameworks such as HIPAA, GDPR, and India’s Digital Personal Data Protection (DPDP) Act. Candidates who can navigate both the computational and clinical aspects of life sciences are emerging as the most competitive professionals in the market. However, the path to becoming a life sciences AI expert comes with its own set of challenges.

Challenges shaping the AI ​​talent landscape

Despite the proliferation of opportunities, AI professionals must navigate challenges to ensure their careers remain unaffected. For example, clinical and genomic datasets often reside in silos, resulting in significant data fragmentation and limiting interoperability and model accuracy. Additionally, bias in the form of inconsistent data collection and labeling practices and lack of standardization can introduce bias into algorithms. Additionally, there are regulatory compliance issues that further complicate model deployment by ensuring compliance with medical privacy laws. Finally, from an organizational perspective, there is still a shortage of hybrid experts who can effectively bridge AI engineering and biological sciences.

These challenges require more than technical expertise. These require a deep understanding of ethical AI practices, transparent data governance, and collaboration across research institutions, regulators, and industry.

The road ahead: collaboration and learning

The future of AI in life sciences will be defined by hybrid roles that blend AI, data science, and precision medicine. Professionals who continually improve their skills, especially in areas such as explainable AI, clinical informatics, and model governance, will be at the forefront of innovation. Strong industry-academia partnerships also play an important role in fostering talent pipelines. Universities and research centers that integrate computational biology, data science, and ethical AI into their curricula will help develop the next generation of AI specialists.

Ultimately, the true potential of AI in life sciences lies in collaboration between data scientists and clinicians, startups and pharmaceutical giants, and between technology and human insight. Companies are integrating AI across research and development, diagnostics, and care delivery to create a more intelligent and responsive healthcare ecosystem. For AI experts and AI aspirants, this is certainly a defining moment, one algorithm and one discovery at a time, shaping the future of life sciences.

Neeti Sharma, CEO, TeamLease Digital