Senior Executive-Data Scientist

Job Responsibility:

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.

Qualification

Education:

• Bachelor’s/Master’s/Ph.D. degree in Data Science, Statistics, Computer Science, Engineering, or a related field.

Experience:

• Minimum 5 years of experience in data science or advanced analytics projects within the pharmaceutical industry with expertise on AI.
• Proven expertise in building and deploying advanced analytics solutions for pharmaceutical operations.
• Experience working with clinical trial data, manufacturing data, and sales data.

Skills:

• Strong proficiency in statistical methods, machine learning, and data analytics.
• Hands-on experience with data science tools and platforms, such as Python, R, SQL, TensorFlow, and PyTorch.
• Excellent data visualization skills with tools like Tableau, Power BI, or similar.
• Knowledge of big data technologies, such as Hadoop, Spark, and NoSQL databases.
• Strong communication and interpersonal skills, with the ability to effectively collaborate with cross-functional teams and stakeholders.
• Analytical mindset with the ability to leverage data to drive decision-making and innovation.
• Basic understanding of data engineering principles and practices.