Data Scientist - Clinical & Operational Analytics
Iqvia Biotech5+ yrsBengaluruOn-siteDirect career pageEst. 18–32 LPA
Posted 3d ago
Reach people at Iqvia Biotech
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at Iqvia Biotech you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Lead clinical and operational advanced analytics initiatives to generate insights from diverse data sources using machine learning and statistical methods.
- Develop, validate, and maintain scalable predictive models and code in Python, R, or equivalent, ensuring lifecycle management through version control.
- Collaborate with business teams to define analytics problems, design integration and data pipelines, and deliver solutions optimizing performance in regulated environments.
Minimum Requirements
- Minimum 5 years experience with a relevant master's degree OR minimum 2 years experience with a Ph.D. in data science, statistics, informatics, operations research, computer science, engineering, or related field.
- Proficiency in Python or R programming and strong knowledge of supervised and unsupervised machine learning methodologies.
- Experience with data engineering, preprocessing, wrangling, and working with structured and unstructured data in complex integration/warehousing environments, preferably in pharmaceutical clinical or regulated industries.
- Proficient in English; certifications in data science or relevant education preferred but not mandatory.
Ideal Candidate Profile
- Experienced in applying advanced analytics and machine learning in regulated industries such as pharmaceuticals with complex data integration challenges.
- Skilled in translating business problems into analytics projects and influencing stakeholders in matrix environments to achieve realistic goals.
- Technically proficient in lifecycle management of code/models, data engineering, and model validation with a strong foundation in traditional statistics and machine learning.