Job Description
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.
Education
QUALIFICATIONS
- 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.
About Us
Amneal is an equal opportunity employer. We do not discriminate based on caste, religion, gender, disability, or any other legally protected status. We believe in fostering a workplace that values diversity and inclusion.
