Data Scientist
Role details
Job location
Tech stack
Job description
- Apply descriptive, predictive, and prescriptive analytics to inform business decisions throughout all stages of clinical study.
- Build advanced statistical models and machine learning tools to support Forecasting, Resource Allocation and Timeliness of clinical trial operational progress and milestones.
- Translate data analysis output into formalized messaging, insights and actions using PowerPoint and communicate effectively to stakeholders.
- Analyze trial related data to support external benchmarking studies, and extract insights from publicly available data sources to inform trial planning.
- Develop a centralized mechanism to analyze, alert & predict the site-, country-, study-, and portfolio-level risks & issues for ongoing and future clinical trial.
- Design the AI/gen-AI process to automate document review and implement chatbot functionality to facilitate data queries based on LLM framework., ADP, Inc. is hiring Lead Data Scientists in our Roseland, NJ location. Are you empathetic to client needs and inspired by transformation and impacting the lives of millions of p…
- 1 day ago
Requirements
The Data Scientist will work in the client group as part of the Data Science Team, and focus on a variety of Data Science projects across the board leveraging data from multiple internal R&D Systems and external Data Vendors enabling Data Driven culture across all R&D. The candidate will have direct accountability to build and productionize data science models and AI/ML tools allowing for proactive risk monitoring, pattern detection and predictive analytics. This is a very technical role, and we are looking for a candidate with strong technical acumen and passion for data science, who stays current on AI/ML development., * Minimum of 5 years' experience as a Data Scientist in Pharmaceutical Industry working with Clinical Trials Operational data / Real World Data.
- Minimum of 5 years' experience of coding in Python, R / R Shiny leveraging data science libraries
- Strong experience in building supervised and unsupervised machine learning methods and deploying them into production (i.e., regression models, random forests, decision trees, NLP, clustering etc.)
- Strong problem-solving skills
Hybrid, 2 to 3 days a week onsite