Pharma Data Science Consultant (Life Sciences)

TL;DR

Pharma Data Science Consultant (Life Sciences): Developing and optimizing machine learning and statistical models for pharmaceutical use cases with an accent on clinical trial data, real-world evidence, and biomarker data. Focus on translating complex healthcare datasets into actionable scientific insights and leading end-to-end data science projects.

Company

Independent technology consulting firm with over 1,000 clients globally, specializing in Information Systems, Telecom, Life Sciences, and Engineering.

What you will do

• Lead end-to-end data science projects from problem definition to model deployment and interpretation.
• Develop, validate, and optimize ML and statistical models specifically for pharma use cases.
• Perform advanced feature engineering on complex healthcare and pharmaceutical datasets.
• Advise stakeholders on data selection and prioritization leveraging domain expertise.
• Collaborate with data engineers and product owners to ensure data and model relevance.
• Communicate methodologies and results clearly to non-technical stakeholders.

Requirements

• Proven experience as a Data Scientist with expertise in modeling and feature engineering.
• Mandatory background in the pharmaceutical or life sciences industry (pharma, biotech, CRO, or healthcare analytics).
• Knowledge of pharma data types: Clinical trial, R&D, biomarker, or Real-world evidence (RWE) data.
• Proficiency in Python and/or R, including pandas, NumPy, and scikit-learn.
• Experience working with structured and semi-structured datasets.
• Ability to bridge technical data science with business and scientific perspectives.

Culture & Benefits

• Dynamic international environment with opportunities to work on diverse projects.
• Strong career development path with training and internal mobility.
• Access to a global community of experts and knowledge-sharing initiatives.
• Involvement in innovative and high-impact projects within the life sciences sector.

Hiring process

• Brief virtual or phone introductory call to discuss motivations.
• Average of 3 interviews with line managers and team members.
• Case study or technical assessment (role play or problem-solving scenario).