Job Description Summary
Work closely with project teams and innovation teams to drive use of statistical and data science methods in combined lab and computational experimental planning, analysis and reporting for the development of drug product formulations. Enable scientists to independently work with basic statistical methods while directly supporting advanced statistical and data science tasks. Apply statistical and data science methods to generate business insights for continuous improvement and enhancing our ways of working.
Job Description
Major accountabilities:
- Project manages own tasks and work with team members; plans proactively, anticipates, and actively manages change,
- Supports teams to set stakeholder expectations as required, identify operational risks and provide insights leading to effective mitigations.
- Collaborate with internal stakeholders, external partners, and cross-functional teams to
solve critical business problems. - Research, develop and/or co-develop new algorithms, methods, statistical models and business models and providing insight into structured and unstructured data.
- Quickly learn to use tools, data sources and analytical techniques needed to answer a wide range of critical business questions
- Assist in building and rolling out tools to enable scientific colleagues to effectively analyze large amounts of data using basic statistical tools with minimal oversight.
- Support the evaluation of novel software, visualization tools and new approaches to statistics and AI to increase efficiency and quality of the Novartis practices.
- Independently identify research articles and propose them for application to Novartis business problems.
- Interface between IT and business needs for lab digitalization.
- Articulate solutions/recommendations to business users.
- Present analytical content concisely and effectively
- Ability to provide understandable and actionable business intelligence for key stakeholders.
Minimum Requirements:
Education and Work Experience:
Minimum: Advanced degree in statistics, data science, physical science, engineering, pharmaceutics or relevant discipline (PhD or equivalent) with a strong focus on DoE, combining experiments and simulations or data science techniques.
Desirable: Ph.D. in scientific or relevant discipline or equivalent
- Experience in pharmaceuticals, food, chemicals, oil and gas or similar.
- Master’s with 8-10 years’ experience or PhDs with 4-6 years’ experience.
- Expert in statistical experimental design such as Design of Experiments, Bayesian optimization etc.
- Expert in multi-variate data analysis
- Interdisciplinary thinking and interest in collaboration with other functions.
- Successfully demonstrated track record of creativity and problem solving in projects.
- Strong presentation skills and scientific/technical writing skills.
- Good project management skills.
- Good communication skills, organizational, planning and negotiation skills.
- Coaching skills
- Curiosit
- Skills (Desirable but not mandatory)
- Knowledge of relevant GLP, GMP regulations and policies.
- Understanding of the development of pharmaceutical formulations including solid understanding of QbD principles
- Applied Mathematics.
- Statistical modelling
- Data Science.
- Data Visualization.
- Languages :
- English.
Skills Desired
Apache Hadoop, Applied Mathematics, Big Data, Curiosity, Data Governance, Data Literacy, Data Management, Data Quality, Data Science, Data Strategy, Data Visualization, Deep Learning, Machine Learning (Ml), Machine Learning Algorithms, Master Data Management, Proteomics, Python (Programming Language), R (Programming Language), Statistical Modeling
