D Cube Analytics - Analytics Lead - DDS Functional (3-6 yrs)
- Work as a functional lead to design and develop modularized products in Pharma analytics space (including commercial analytics as well as clinical research analytics).
- As a functional lead, should possess a very good pharma domain knowledge on various therapy areas and also need to have a good understanding, work experience on a wide spectrum of pharma data assets (including but limited to Sales, Claims (RWD), EHR/EMR, Publications, Formulary, Medial vocabularies etc.)
- Should amalgamate business (pharma domain) & data knowledge with analytics & technology skills to come up with a thought process of converting a custom analytics projects into a standardized, dynamic and scalable product that can be used across customers.
- Work closely with analysts, technical team members and customers to build systems for effective data exploration and consumption. Work in an agile, SCRUM driven environment to deliver new, innovative products/modules.
- Design, develop, and deliver AI/machine learning enabled solutions for our industry specific data analytics platform.
- Develop working prototypes of algorithms and evaluate and compare metrics based on the real- world data sets Provide design input specifications, requirements, and guidance to software engineers for algorithm implementation for solution/product development.
- Design, implement and support efficient & reliable data pipelines to move data from a wide variety of data sources to map to our data models Design and implement data aggregation, cleansing and reporting layers
- Build scalable, available, and supportable processes to collect, manipulate, present, and analyze large datasets in a production environment
- Articulate problem definition and work on all aspects of data including acquisition, exploration/visualization, feature engineering, experimentation with machine learning algorithms, deploying models.
Note : ONLY THOSE WITH HANDS-ON ANALYTICS EXPERIENCE WITH PHARMA-RELATED DATA SETS CAN APPLY (We need people who have worked with real-world data in the pharma domain; patient level data etc.)