Job Description

Employment Type: Full time

Company Information

At Lilly, we unite caring with discovery to make life better for people around the world. We are a global healthcare leader headquartered in Indianapolis, Indiana. Our employees around the world work to discover and bring life-changing medicines to those who need them, improve the understanding and management of disease, and give back to our communities through philanthropy and volunteerism. We give our best effort to our work, and we put people first. We’re looking for people who are determined to make life better for people around the world.

Position Overview

Within the Global Statistics organization, Advanced Analytics and Access Capabilities is the global center of expertise for all statistical aspects of Patient Access, Health Technology Assessment (HTA) and Real-World Evidence (RWE). Within this center, the HTA Statistics Team provides input and statistical leadership to evidence generation plans and Access & Reimbursement projects to accelerate patient access in collaboration with multiple partners from other functions, geographies, and affiliations.

Responsibilities

General Scope

The job tasks listed below outline the scope of the position. The application of these tasks may vary, based upon current business needs.

• Be a successful problem solver who can bring to bear technical expertise to work closely with other functions to answer important questions about our products by translating strategic needs into innovative statistical solutions.
• Provide technical statistical leadership to data-related research in Health Technology Assessment (HTA) and reimbursement to improve the success of the compound, considering requirements across the globe (US, Europe, Asia Pacific, and other geographies).
• Utilize the most efficient or innovative approach in economic modeling, (network)-meta-analysis and indirect comparisons, and clinical trials.
• Stay up-to-date with respect to statistical methodology, particularly in methods applicable to the research area of HTA, to maintain proficiency in applying new and varied methods, and to be competent in justifying methods selected.
• Ensure high-quality statistical support for projects through the oversight of external suppliers and provide input into outsourcing strategy and processes.
• Influence cross-functional team members regarding appropriate research methods.

Communication of Results and Inferences

• Collaborate with team members to write reports and communicate results.
• Assist with, or be responsible for, communicating study results via payer submissions, publications, or oral presentations in group settings, as well as for communicating one-on-one with key customers and presenting at scientific meetings.
• Support responses to payer queries and/or interactions.
• Actively contribute to internal and external scientific interactions with decision-making agencies and healthcare professionals.

Therapeutic Area Knowledge

• Understand disease states, competitive landscapes, and regulatory/payer environments to enhance the level of customer focus and collaboration as a strong scientific contributor.

Statistical Leadership and Teamwork

• Introduce and apply innovative methodology and tools to solve critical problems.
• Merge scientific thinking and business knowledge to identify issues, evaluate options, and implement solutions.
• Lead projects independently and work effectively across functions.
• Apply technical expertise to influence business decisions.
• Actively contribute to brand strategy and portfolio decisions in cross-functional governance bodies.

Qualifications

Basic Qualification

• PhD with 3+ years of experience or MSc degree with 6+ years of experience in statistics or a related field.

Additional Preferences

• Expertise in HTA, health outcomes research, and/or real-world evidence is required.
• Significant experience in Health Technology Assessment in major markets.
• Excellent knowledge of statistical theory and methodology, with a solid understanding of experimental design and causal inference.