Job Location: Bengaluru
The Associate Data Scientist is responsible for managing data analysis, modeling, and visualization with a reasonable level of oversight from management and/or more experienced peer mentors.
Data Modeling and Machine Learning (40%)
Starts to gain proficiency and understanding of various Early Development databases, data structures, and data standards related to non-clinical sales, scheduling, study planning and design, reporting, and document and content management.
Develops proof-of-concept and/or prototype programs that utilize data to automate work (e.g. protocol development) by utilizing predictive (e.g. anticipated non-clinical study results based on historical biochemical response) and prescriptive (e.g. recommend study design and data endpoints based on predicted non-clinical outcomes) models and statistical methods.
Advises management on new technology, automation, and/or processes that will reduce errors, improve productivity, and/or quality.
Data Visualization and Storytelling (40%)
Discover stories told by data that have the potential to positively or negatively influence drug and/or business development.
May attend industry software and/or programming meetings to stay informed of industry trends, and influential decisions related to the scope of data and technology responsibilities (e.g. AI-ML Drug Discovery & Development Summit, Artificial Intelligence for Early Drug Discovery conference, AI in Pharmaceuticals Summit etc.).
Systems Design, Integration and Automation Systems (10%)
Uncovers novel opportunities for connecting disparate data sources together to automate data transfer and population using Extract, Translate, and Load (ETL) principles.
Recommends reliable data flow process that ensure any key-entered data is entered only once into a source system and then automatically flows to other applications as needed.
Design Thinking (10%)
Starts to build and foster a network of relationships with business end users to develop iterative data solutions through the use of the five design thinking steps – Empathize, Define, Ideate, Prototype and Test.
Liaises with one or more stakeholder groups (e.g. Study Directors, Data Engineers, and Department, Business Unit, or Executive Leadership) to drive business engagement.
Learns change management tactics and how to assess business readiness to ensure that solutions are widely adopted, value added, and impactful.
- 4+ years of coursework or equivalent experience in applicable field(s).
- Solid knowledge of statistics and probability
- Solid programming skills in R, Python, Matlab, SAS, or equivalent.
- Knowledge of relational databases and SQL.
- Strong communication skills and ability to present science ideas and results effectively
- Good understanding and hands-on experience with data mining, machine learning and optimization techniques.
- Able to quickly prototype data science models
- Experience with NoSQL databases and other big data technologies.
- Experience with life science and/or healthcare data.
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