Celgene is a global biopharmaceutical company leading the way in medical innovation to help patients live longer, better lives. Our purpose as a company is to discover and develop therapies that will change the course of human health. We value our passion for patients, quest for innovation, spirit of independence and love of challenge. With a presence in more than 70 countries, and growing – we look for talented people to grow our business, advance our science and contribute to our unique culture.
Celgene’s Informatics and Predictive Sciences (IPS) department seeks a talented, collaborative computational researcher to drive application of statistics, machine learning and data modeling for identification of critical relationships between manufacturing processes and key therapeutic product characteristics.
In close collaboration with colleagues in Celgene’s Global Product Development Organization (GPDO), this role will make a significant contribution to data-driven optimization of product development and manufacturing across our portfolio, from novel therapies in clinical trials to some of Celgene’s best-known cancer and autoimmune disease therapies.
Early areas of focus will include the study of predictive monitoring for product properties of interest during the manufacturing process, such as batch consistency, release timing, and variation across manufacturing platforms. Datasets will originate from a developing infrastructure to capture the properties of starting materials, manufacturing process parameters, and measurements from in-line sensors and post-manufacturing testing. Algorithmic approaches will include unsupervised learning and outlier detection, active and supervised learning, and both mechanistic and data-driven modeling, alongside data integration and biological/chemical interpretation methods.
Beyond these initial focus areas, the role will contribute also to building a predictive culture within GPDO and take responsibility for identifying and acting upon opportunities for applied analytical research that yield tangible benefit to our patients. Key collaborators and stakeholders will include product manufacturing and development teams in Switzerland, New Jersey and Arizona, and IPS colleagues engaged in a variety of analytics and modeling projects across the business.
An additional component of the role will be to partner with computational research, knowledge science and IT colleagues to align data generation with infrastructure, data integration strategies, and innovative approaches that enable full leverage of molecular & cellular profiling, chemical structure, manufacturing process, clinical outcomes, and real-world evidence datasets across GPDO and beyond.
The position would suit an individual with high scientific leadership potential and excellent communication and collaboration skills. Keen interest and hands-on expertise in the inter-disciplinary application of advanced analytical methods to life sciences and industrial operations data are imperative.
Applications are encouraged from those looking to impact delivery of truly innovative and life-changing therapies for complex diseases of unmet medical need.
Responsibilities include but are not limited to:
- Develop algorithmic and data-driven approaches aligned to manufacturing science objectives, including leverage of leading methods from related fields.
- Engage directly with colleagues and as part of project teams focused on optimizing our manufacture of small molecule therapies, lending analytics expertise as required.
- Collaborate with IPS and GPDO colleagues to identify areas of synergy and integration of data and methods applied in other business functions, and novel therapeutic properties from our emerging pipeline.
- Contribute to broader data analysis and predictive methods strategies across the business as required, including assessment of 3rd party capabilities.
- Present strategies, approaches, results and conclusions to publishable standard, frequently for an audience with complementary expertise.
- Contribute to enable strategic collaborations with academic and commercial collaborators to benefit therapeutic programs.
- Catalyze a predictive culture across the GPDO function, by demonstrating benefits of leading analytical research in key application scenarios.
Background experience & complementary knowledge
- PhD in computer science, mathematics, statistics, or related fields from a recognized higher-education establishment (Master’s Degree with substantial expertise may be possible)
- 7+ years post-doctoral experience of predictive analytical research in university, clinical, pharmaceutical or biotechnology environments.
- Publication track-record in use of analytical methods to elucidate and drive decisions in complex research scenarios.
- Proven experience in development and application of novel algorithmic approaches to identify patterns in complex datasets and/or accelerate data-driven decisions.
- In-depth knowledge of contemporary machine learning, pattern recognition and data-mining techniques, paradigms and application scenarios.
- Hands-on experience of data integration, mining & visualization, and development of multivariate models in the business operations context.
- Experience with real-time data collection and analysis preferred.
- Background knowledge in manufacturing operations preferred.
- Background knowledge in variance components analysis and lean and 6-sigma approaches preferred.
- Strong knowledge of contemporary data and computing infrastructures, open-source analytics tools and database structures.
- Proven problem-solving skills, collaborative nature and adaptability across disciplines.
- Excellent verbal and written communication skills. Fluent verbal and written English language skills prerequisite.
This job is posted by Celgene.
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