Senior Consultant - Data Science, Commercial Services Analytics US Dedicated

Description

The company is a global healthcare analytics, technology and consulting firm that supports clinical research, commercial insight and healthcare intelligence for life sciences and healthcare clients.

This is a senior data science role on a team that turns complex healthcare data into insight, working alongside researchers, consultants and technical specialists from several disciplines.

Responsibilities

• Build and run statistical and quantitative analyses on large, complicated healthcare datasets.
• Use mathematical and statistical models to find the main drivers and relationships in the data, measure how changing market or clinical conditions affect outcomes, and forecast trends.
• Turn analytical results into clear, practical business insights for internal and external stakeholders.
• Help design and carry out custom research studies, including analysis, interpretation and synthesis of findings.
• Help prepare research findings, reports and presentations, including those meant for publication.
• Work with cross-functional colleagues to deliver high-quality analytics.

Requirements

• Bachelor's degree in mathematics, statistics, data science or another technical or quantitative field.
• Working knowledge of statistical programming in Python or R.
• Experience applying machine learning, clustering and segmentation, and time-series analysis.
• A record of turning complex analysis into conclusions the business can use.
• Strong written and verbal communication, including presenting findings clearly.
• Fluency in English, written and spoken.
• Preferred: interest in healthcare, life sciences or real-world evidence analytics.
• Preferred: comfort working in a fast-moving, collaborative environment with a focus on clients.
• Preferred: careful attention to detail and a commitment to rigorous analysis.
• Preferred: curiosity and openness to learning new methods and subject areas.

Conditions

The company applies equal treatment between women and men and values employees from varied backgrounds.