Senior Executive-Data Scientist

Data Analysis and Modeling:

• Develop and implement advanced statistical models, machine learning algorithms, and data mining techniques to analyze complex pharmaceutical data.
• Perform exploratory data analysis to identify trends, patterns, and insights that can drive business decisions.
• Create predictive models to forecast demand, optimize supply chain processes, and improve product quality.

Advanced Analytics:

• Utilize advanced analytics techniques to optimize manufacturing processes, enhance quality control, and improve overall operational efficiency.
• Conduct root cause analysis using data-driven approaches to identify and resolve issues in the manufacturing process.
• Implement natural language processing (NLP) techniques to analyse textual data from various sources.

AI & Gen AI Solutions:

• Explore and implement generative AI solutions to drive innovation and enhance operational processes.
• Lead the development and deployment of AI-based use cases, including predictive analytics, image recognition, and NLP solutions.
• Develop Gen AI models for applications Energy Management, Performance Insights, Root cause and Investigation Advisor etc.
• Stay updated with the latest advancements in generative AI and identify potential use cases within the organization.

Data Management:

• Work with large datasets, including clinical trial data, manufacturing data, and sales data, to extract meaningful insights.
• Develop and maintain data pipelines and ETL processes to support data science initiatives.

Stakeholder Collaboration:

• Work closely with cross-functional teams, including operations, QA, R&D, and marketing, to understand business needs and deliver data-driven solutions.
• Communicate complex data findings and insights to non-technical stakeholders in a clear and actionable manner.
• Collaborate with external partners, such as research institutions and technology providers, to stay abreast of industry advancements.

Innovation and Continuous Improvement:

• Stay updated with the latest trends and advancements in data science, machine learning, and artificial intelligence.
• Identify opportunities for process improvement and innovation through the application of data science techniques.
• Promote a culture of continuous learning and improvement.

Key Projects and Initiatives:

• Predictive Analytics: Develop predictive models to forecast product demand, optimize inventory levels, and improve supply chain efficiency.
• Quality Control: Implement data-driven approaches to monitor and improve product quality, reduce defects, and ensure compliance with regulatory standards.
• Operations Data Analysis: Analyze operations data to identify trends, optimize trial design, and support regulatory submissions.
• Process Optimization: Use advanced analytics techniques to enhance manufacturing processes, reduce operational costs, and increase productivity.
• Natural Language Processing: Apply NLP techniques to analyze textual data from research publications, patents, and customer feedback.