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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.
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Data Modeling and Machine Learning (40%)
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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.
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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.
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Advises management on new technology, automation, and/or processes that will reduce errors, improve productivity, and/or quality.
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Data Visualization and Storytelling (40%)
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Discover stories told by data that have the potential to positively or negatively influence drug and/or business development.
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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.).
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Systems Design, Integration and Automation Systems (10%)
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Uncovers novel opportunities for connecting disparate data sources together to automate data transfer and population using Extract, Translate, and Load (ETL) principles.
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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.
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Design Thinking (10%)
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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.
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Liaises with one or more stakeholder groups (e.g. Study Directors, Data Engineers, and Department, Business Unit, or Executive Leadership) to drive business engagement.
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Learns change management tactics and how to assess business readiness to ensure that solutions are widely adopted, value added, and impactful.
II. Experience:
- 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
Preferred:
- 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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