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.