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Associate Data Scientist

Trinitylifesciences
BengaluruEntry level2–3 yrs
Apply on Trinitylifesciences’s site

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Associate, Data Scientist
Bengaluru · found 1 d ago

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The job in short

Data science.

PythonMarketingData science

Details

Found
6 d ago · posted 2026-09-28
Location as listed
India - Bangalore; India - Gurgaon
Role
Data Scientist
Source
The company’s own careers site

Full description

Job Objective: Design, Develop, Deploy and Maintain - Predictive models based on Business Case. Handle end to end projects in Marketing Analytics for Life Science companies Roles & Responsibilities: • Work with internal business teams to understand problem statement, requirements and use cases • Work on Business problems including Marketing Effectiveness, Sales Engagement & Next Best Action, Demand Planning, Customer Segmentation, Test & Control analysis • Conduct simple and advanced data analysis to help clients make good business decisions • Apply advanced statistical, econometric, optimization models and algorithms to real-world business problems • Explore emerging statistical models as well as provide analysis using existing models • Build solutions on Structured as well as Un-structured data like Text, Images etc. • Maintain the highest standards to follow the best practices and trending analytics in pharma domain • Work with project leadership to define project scope Requirements: • Masters in Statistics, Marketing, Finance, Operations research   • 2-3 years of work experience on analytic work (use of advanced modelling projects and tools such as R, Python etc.). Knowledge of promotional channels and digital analytics. • Strong analytical skills, excellent communication skills and the ability to communicate actionable analytical findings. Familiarity with the application of analytics to sales, marketing, or managed markets problems. • Ability and desire to work in a proactive, highly engaging, high-pressure, client service environment. • Should have sound experience in data mining and data analysis. Additional Skills: • Knowledge on various commonly used transactional data assets and others including Volumes/ Value, Rx, Specialty level, Longitudinal Patient level data assets

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