Consultant – Patient Analytics

This is a fully remote position, open to applicants in India.

📋 Description

• Assisting in the formulation and implementation of diverse analytics and data initiatives aimed at enhancing the commercial activities of pharmaceutical and life science companies.

• Implementing patient solutions, creating innovative approaches by leveraging new data sets, and addressing specific disease-focused commercial challenges.

• Crafting and deploying advanced analytics solutions tailored to meet particular patient-oriented business requirements.

• Collaborating closely with global project leaders and engagement managers, along with team members, to perform high-quality analyses and produce well-organized, impactful deliverables.

• Employing analytical thinking and a structured methodology to comprehend issues, evaluate data, and outline suitable solutions.

• Guiding and mentoring team members on analytical methodologies, data sets, and relevant commercial applications.

⛳️ Requirements

• Minimum of 4 years of pertinent experience in delivering data and analytics projects within the Pharmaceutical/Healthcare sector.

• Preferably, 2+ years of experience in data science and/or patient analytics.

• Familiarity with advanced analytics methods for predictive analytics.

• Proficient in machine learning, statistical analysis, and data manipulation utilizing Python, R, or other cutting-edge tools.

• Strong proficiency in analytics and visualization tools (e.g., Python, R, Alteryx, SAS, Tableau, PowerBI).

• Outstanding communication skills with the capability to tackle unstructured real-world challenges.

• Deep interest in understanding and crafting solutions for current challenges within the pharmaceutical industry.

• Proven ability to coach and mentor junior team members.

• Capability to function effectively in an international matrix environment and to collaborate with US clients.

🏝️ Benefits

• Contribute to the growth of the firm.

• Foster innovation by keeping up with emerging trends and best practices in data science.

• Engage in firm-wide initiatives, including the development of new capabilities.

• Build and sustain a comprehensive knowledge repository.