Retained Search

Location: Central New Jersey (2-3 days a week / Remote to start)
Compensation: ~$350K+ All-in

About Role:
This is a new position. You will join a new cutting-edge Data Science team to advance the global drug development process. We are looking for candidates with experience modeling complex clinical, real-world data (RWD) using the latest NLP techniques/algorithms including Deep Learning.

Ideal candidates will possess relevant PhD-level training and have 4-10 years of experience as a Data Scientist.   Experience modeling Real World Data and Clinical Trials Data is a big plus. 

What you’ll do:

• Work with stakeholder to develop, implement and apply state-of-the-art algorithms to address key business problems
• Develop novel ways of integrating, mining, and visualizing diverse, high dimensional and disparate data sets
• Drive the development and implementation of innovative statistical methods, novel trial designs, etc.
• Drives translation of digital health analytics; use of modern data processing learning capabilities including ML, AI, deep learning, and beyond.
• Formulates, implements, tests, and validates predictive models and implements efficient automated processes for producing modeling results at scale.
• Responsible for collaborating with cross-functional teams, including but not limited to, clinicians, data scientists, translational medicine scientists, statisticians, and IT professionals.
• Manages and coordinates limited resources to produce quality deliverables within timelines for competing priorities.

Key Requirements:

• Ph.D. in a relevant quant field (i.e. Computational Biology, Computation Linguistics, Biostatistics, Statistics, Computer Science, etc.) and 10+ years of relevant experience
• Mastery in data analysis and modeling methods particularly in their application to pharma R&D Experience in the application of AI/ML, and proficient in SQL, Python, and R
• Significant industry and track record of leading statistical innovation
• Perspective in leveraging innovative approaches to expedite drug development and address the complexities of emerging data