Principal Data Scientist at GSK

Bengaluru, karnataka, India -

Full Time

Start Date

Immediate

Expiry Date

24 Jul, 26

Salary

0.0

Posted On

25 Apr, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Science, Machine Learning, Artificial Intelligence, Python, Scikit-learn, PyTorch, TensorFlow, SQL, Cloud Platforms, AWS, Azure, GCP, Data Engineering, Model Deployment, Mentoring, Communication

Industry

Pharmaceutical Manufacturing

Description

Site Name: Bengaluru Luxor North Tower Posted Date: Apr 25 2026 Business Introduction GSK remains committed to achieving bold commercial ambitions for the future. By 2031, we aim to deliver £40 billion in annual sales, leveraging our existing strong performance momentum to significantly increase our positive impact on the health of billions of patients globally. Our Ahead Together strategy is centred on early intervention to prevent and alter the course of disease, thereby protecting people and supporting healthcare systems. Our diverse portfolio consists of vaccines, specialty medicines, and general medicines. Through continuous innovation and a dedicated focus on scientific and technical excellence, we strive to develop and launch new, groundbreaking treatments that address critical health challenges. Position Summary You will lead data science work that delivers measurable impact across teams. You will design and build advanced machine learning and AI solutions. You will work closely with product owners, engineers and business partners to turn data into clear decisions. We value curiosity, technical depth, collaboration and ethical thinking. This role offers strong growth, visible impact and the chance to help GSK unite science, technology and talent to get ahead of disease together. Responsibilities This role will provide YOU the opportunity to lead key activities to progress YOUR career. These responsibilities include some of the following: - Lead end-to-end development of machine learning and AI solutions, from problem definition to production deployment. - Translate business questions into technical approaches and clear, actionable insights for stakeholders. - Build and validate models using modern techniques in supervised, unsupervised and deep learning. - Work with engineers to deploy models reliably using cloud platforms and production standards. - Mentor and coach other data scientists to grow skills and strengthen the team’s practices. - Champion responsible and reproducible AI practices, including model validation, documentation and bias awareness. Why You? We are looking for people who want to solve important problems with data. This role is offered as a hybrid position based in India, with a mix of office and remote work depending on team needs. Basic Qualification We are seeking professionals with the following required skills and qualifications to help us achieve our goals - Bachelor’s or Master’s degree in data science, computer science, statistics, mathematics, engineering or a related quantitative field. - 7+ years of professional experience in data science, machine learning or related roles. - Strong programming skills in Python and experience with relevant libraries (for example scikit-learn, PyTorch, TensorFlow). - Practical experience with data engineering tools and databases, including SQL and at least one cloud platform (AWS, Azure or GCP). - Proven track record deploying models to production and working with software engineering teams. - Clear communicator who can explain technical work to non-technical partners and present insights with concise visualisations. Preferred Qualification If you have the following characteristics, it would be a plus - Hands-on experience with large language models, retrieval-augmented generation or other generative AI techniques. - Experience with MLOps or LLMops tools for model monitoring, CI/CD and lifecycle management. - Familiarity with multi-modal models (text, image, audio) or computer vision pipelines. - Experience with vector databases, semantic search or information retrieval systems. - Advanced degree (PhD or Master’s) in a quantitative subject or demonstrable equivalent experience. - Previous work in healthcare, life sciences or related regulated industries. What we offer You will join a collaborative team focused on meaningful outcomes. You will get the chance to grow your technical and leadership skills. You will work on problems that matter to patients and health systems. We encourage people from all backgrounds to apply. If you need adjustments during recruitment, please share your needs and we will work with you. Apply now If this role excites you, please apply. Share your CV and a short note about a data science project you led and the impact it delivered. We look forward to hearing from you. Why GSK? Uniting science, technology and talent to get ahead of disease together. GSK is a global biopharma company with a purpose to unite science, technology and talent to get ahead of disease together. We aim to positively impact the health of 2.5 billion people by the end of the decade, as a successful, growing company where people can thrive. We get ahead of disease by preventing and treating it with innovation in specialty medicines and vaccines. We focus on four therapeutic areas: respiratory, immunology and inflammation; oncology; HIV; and infectious diseases – to impact health at scale. People and patients around the world count on the medicines and vaccines we make, so we’re committed to creating an environment where our people can thrive and focus on what matters most. Our culture of being ambitious for patients, accountable for impact and doing the right thing is the foundation for how, together, we deliver for patients, shareholders and our people. Inclusion at GSK: As an employer committed to Inclusion, we encourage you to reach out if you need any adjustments during the recruitment process. Please contact our Recruitment Team at IN.recruitment-adjustments@gsk.com to discuss your needs. Important notice to Employment businesses/ Agencies GSK does not accept referrals from employment businesses and/or employment agencies in respect of the vacancies posted on this site. All employment businesses/agencies are required to contact GSK's commercial and general procurement/human resources department to obtain prior written authorization before referring any candidates to GSK. The obtaining of prior written authorization is a condition precedent to any agreement (verbal or written) between the employment business/ agency and GSK. In the absence of such written authorization being obtained any actions undertaken by the employment business/agency shall be deemed to have been performed without the consent or contractual agreement of GSK. GSK shall therefore not be liable for any fees arising from such actions or any fees arising from any referrals by employment businesses/agencies in respect of the vacancies posted on this site. It has come to our attention that the names of GlaxoSmithKline or GSK or our group companies are being used in connection with bogus job advertisements or through unsolicited emails asking candidates to make some payments for recruitment opportunities and interview. Please be advised that such advertisements and emails are not connected with the GlaxoSmithKline group in any way. GlaxoSmithKline does not charge any fee whatsoever for recruitment process. Please do not make payments to any individuals / entities in connection with recruitment with any GlaxoSmithKline (or GSK) group company at any worldwide location. Even if they claim that the money is refundable. If you come across unsolicited email from email addresses not ending in gsk.com or job advertisements which state that you should contact an email address that does not end in “gsk.com”, you should disregard the same and inform us by emailing askus@gsk.com, so that we can confirm to you if the job is genuine.

Responsibilities

Lead the end-to-end development of machine learning and AI solutions from problem definition to production deployment. Collaborate with cross-functional teams to translate business questions into actionable technical insights and mentor other data scientists.