Key Responsibilities

• Lead NLP Initiatives: Drive development and implementation of advanced NLP models to analyze unstructured text data related to clinical monitoring, quality, and risk management.

• Build and Optimize Predictive Models: Apply machine learning techniques to identify patterns and trends, with a focus on preventing/corrective actions for improving quality assurance practices.

• Collaborate Across Teams: Partner with cross-functional teams, including other data science business and tech teams within GSK, quality assurance, and clinical operations, to understand and solve critical business challenges.

• Ensure Model Governance: Implement and monitor best practices for model governance, accuracy, and reliability in a highly regulated environment, ensuring adherence to industry standards and regulatory requirements.

• Data Preprocessing and Cleaning: Oversee data acquisition, preprocessing, and quality control of complex datasets, working with structured and unstructured data in the R&D domain.

• Lead Data Science Projects: Mentor and guide junior data scientists and analysts, ensuring robust project management, timely delivery, and effective stakeholder communication.

• Continuous Improvement: Identify opportunities to improve data workflows, tooling, and processes for enhanced productivity and reproducibility.

• Research and Innovation: Stay abreast of industry trends and advancements in NLP, machine learning, and generative AI to drive innovation within the quality and risk management framework.

• Performance Metrics and Reporting: Establish and track key performance indicators to assess model impact and value, aligning outcomes with organizational quality and risk management goals.

• Develop Gen AI Solutions: Explore and integrate generative AI models to innovate on complex language tasks such as summarization, data synthesis, and anomaly detection.

Education Requirements

A bachelor's degree in computer science, statistics, mathematics

Job Related Experience

• Proven experience in data science, predictive modelling, and statistical analysis.

• Proficiency in programming languages commonly used in data science, such as Python, R, and SQL.

• Experience with data visualization tools like Power BI, Shiny Web Apps, and similar platforms.

• Application of machine learning algorithms and statistical modelling techniques.

• Natural Language Processing (NLP) to derive actionable insights.

• Strong problem-solving skills and the ability to translate business problems into analytical use-cases.

• Excellent communication skills to effectively interact with business stakeholders and tech partners.

• A good understanding of drug research and development and quality

Other Job-Related Skills

• Advanced knowledge of analytics tools and capabilities: The candidate should be proficient in using advanced analytics tools and be capable of leveraging them to analyse complex data sets.

• Strong understanding of R&D Quality and Risk Management: The candidate should have a deep understanding of quality and risk management within an R&D context.

• Data Analysis Skills: The candidate should be able to interpret complex data and translate it into information that can be understood by non-technical stakeholders.

• Communication Skills: The candidate should have strong communication skills to effectively convey data insights to business stakeholders.

• Problem-Solving Skills: The candidate should have strong problem-solving skills to translate business problems into analytical use-cases.

• Technical Skills: Proficiency in programming languages commonly used in data science, such as Python, R, and SQL, and experience with data visualization tools like Power BI, Shiny Web Apps, and similar platforms.

• Knowledge of Machine Learning: The candidate should have a deep understanding of machine learning algorithms and statistical modelling techniques.

• Familiarity with Natural Language Processing (NLP): The candidate should have knowledge of NLP to handle use-cases in areas like text summarizations, sentiment analysis, topic modelling, and trend analysis.

• Leadership Skills: The candidate should have excellent leadership skills to effectively interact with business stakeholders and tech partners, and to drive the implementation of data science tools and solutions in a matrixed environment.

Why GSK?

Uniting science, technology and talent to get ahead of disease together.

GSK is a global biopharma company with a special purpose – to unite science, technology and talent to get ahead of disease together – so we can positively impact the health of billions of people and deliver stronger, more sustainable shareholder returns – as an organisation where people can thrive. We prevent and treat disease with vaccines, specialty and general medicines. We focus on the science of the immune system and the use of new platform and data technologies, investing in four core therapeutic areas (infectious diseases, HIV, respiratory/ immunology and oncology).

Our success absolutely depends on our people. While getting ahead of disease together is about our ambition for patients and shareholders, it’s also about making GSK a place where people can thrive. We want GSK to be a place where people feel inspired, encouraged and challenged to be the best they can be. A place where they can be themselves – feeling welcome, valued, and included. Where they can keep growing and look after their wellbeing. So, if you share our ambition, join us at this exciting moment in our journey to get Ahead Together.

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