How AI is Redefining Talent and Innovation in Life Sciences

Much like other sectors, AI has become indispensable in the life sciences sector, accelerating progress in healthcare, pharmaceuticals, genomics, and biotechnology. To put things in perspective, the global AI market in the pharmaceutical industry is projected to reach $16.49 billion by 2034, while AI in the medical devices segment is expected to scale up to $97.1 billion by 2028, fuelled by the widespread use of AI-powered diagnostic systems, smart wearables, and surgical robotics.

Across this fast-changing landscape, AI professionals are becoming the most sought-after by organisations in the life sciences segment. These experts are redefining how diseases are diagnosed, drugs are discovered, and care is delivered. For India’s life sciences ecosystem, this is both an opportunity and an inflexion point; one where the convergence of biology, data, and machine learning is opening up entirely new career frontiers. That said, it’s essential for the existing workforce in this field, as well as aspirants, to understand just how the job landscape is likely to evolve and the challenges it may present. 

Rise of AI-driven healthcare and drug discovery roles

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 underpins innovation across the healthcare value chain.

Pharmaceutical companies are increasingly recruiting AI research scientists, machine learning engineers, computational biologists, and bioinformatics specialists to accelerate drug discovery and enhance clinical development. These experts design algorithms that predict molecule behaviour, assess drug efficacy, and shorten R&D timelines; activities that once took years now progress within months.

Simultaneously, digital health and biotech startups are leveraging AI to design remote care solutions, develop imaging software, and analyse high-volume health data. In these setups, AI engineers and data scientists collaborate with clinicians to refine diagnostic accuracy and create more personalised treatment options.

Growing demand across pharma, biotech, and health tech 

India’s life sciences companies and Contract Research Organisations (CROs) are scaling their AI hiring significantly. Bengaluru, Hyderabad, Pune, Mumbai, and Delhi-NCR have emerged as key employment hubs for AI professionals in this space.

The following are some of the key range of applications:

• Pharmaceuticals and biotechnology: AI accelerates target identification, molecular design, and drug–target interaction prediction. It enhances efficacy and toxicity assessments and facilitates personalised medicine through integrated genetic and clinical data.
• Clinical trials: Algorithms now streamline patient recruitment, site selection, and trial management, improving efficiency and reducing costs.
• Manufacturing and supply chain: Predictive Analytics is improving maintenance planning, regulatory compliance, and demand forecasting.
• MedTech: AI powers device simulation, imaging, predictive maintenance, and generative design of patient-specific medical devices, creating entirely new revenue streams.
• Academic medical centres: From immersive learning tools to automated research management, AI is modernising how medical research and education are conducted.

Together, these use cases underscore a new paradigm where AI is central to value creation across the life sciences ecosystem. Within this landscape, the growing integration of AI across research, clinical development, and operations has created a surge in demand for professionals who can ensure technological innovations become tangible scientific and business outcomes.

Expanding spectrum of AI roles

AI expertise in life sciences is no longer confined to data science or research labs. Organisations are hiring across a wide spectrum of roles that blend deep technical skill with scientific insight. Compensation reflects both specialisation and seniority. In roles such as Bioinformatics Scientist, ML Engineer, and Data Scientist, entry-level professionals typically earn between Rs 3 lakh and Rs 12 lakh per annum (LPA), depending on the role and expertise area. Mid-level specialists, i.e., those with three to seven years of experience, command salaries ranging from Rs 8 - Rs 30 LPA, while senior professionals and research leaders can earn anywhere from Rs 18 LPA to upwards of Rs 60 LPA.

Key roles driving this demand include bioinformatics scientists and engineers (Rs 3–Rs 35 LPA), machine learning engineers in life sciences, imaging, or genomics (Rs 6– Rs 50 LPA), data scientists in clinical or pharmacovigilance domains (Rs 5– Rs 40 LPA), and AI research scientists focused on drug discovery or computational biology (Rs 12– Rs 60 LPA). Other high-value positions, such as computational chemists, structural biology engineers, MLOps or AI deployment engineers, and clinical AI managers, typically fall within the Rs 12– Rs 50 LPA range, with leadership roles extending higher.

These figures underscore how interdisciplinary expertise, i.e., combining AI proficiency with biological or clinical acumen, can help one build a solid career in the life sciences sector. Senior professionals who can lead teams at the intersection of AI, drug development, and regulatory science are commanding premium compensation as the industry matures. Now, while the remuneration is impressive, it takes certain specific skill sets for one to climb the ladder in this segment. 

Skills that define the new-age AI professional

Employers are now prioritising candidates with cross-disciplinary expertise—those who can blend computational skill with biological understanding. 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, domain skills in genomics, proteomics, pharmacovigilance, and clinical data analytics are equally valuable. An added advantage would be an understanding of regulatory frameworks such as HIPAA, GDPR, and India’s Digital Personal Data Protection (DPDP) Act. Candidates who can navigate both the computational and clinical dimensions of life sciences are emerging as the most competitive professionals in the market. The road to becoming an AI expert in life sciences, however, comes with its fair share of challenges.

Challenges that shape the AI talent landscape

Despite a surge in opportunities, AI professionals must navigate various challenges to ensure their careers remain unaffected. For instance, there is significant data fragmentation, with clinical and genomic datasets often residing in silos, limiting interoperability and model accuracy. Additionally, bias and a lack of standardisation in the form of inconsistent data collection and labelling practices can lead to biased algorithms. Then there’s the issue of regulatory compliance, where ensuring adherence to healthcare privacy laws adds complexity to model deployment. Finally, from an organisational standpoint, there remains a talent shortage of hybrid professionals who can effectively bridge AI engineering and biological science.

These challenges demand more than technical expertise. They require an in-depth understanding of ethical AI practices, transparent data governance, and collaboration across research institutions, regulators, and industry.

Road ahead: Collaboration and learning 

The future of AI in life sciences will likely be defined by hybrid roles that merge AI, data science, and precision medicine. Professionals who continuously upskill, particularly in areas like explainable AI, clinical informatics, and model governance, will find themselves at the forefront of innovation. Strong industry-academia partnerships will also play a critical role in nurturing talent pipelines. Universities and research centres that integrate computational biology, data science, and ethical AI into their curricula will help prepare the next generation of AI specialists. 

Ultimately, the true potential of AI in life sciences lies in collaboration between data scientists and clinicians, between startups and pharmaceutical giants, and between technology and human insight. As companies integrate AI across R&D, diagnostics, and care delivery, they are building a more intelligent and responsive healthcare ecosystem. For AI professionals and aspirants, this is certainly a defining moment, shaping the future of life sciences, one algorithm and one discovery at a time.

Neeti Sharma, CEO, TeamLease Digital