CustomerInsights.AI - Lead Data Scientist

Description:

About Us

You will be working for one of our incubation clients, a healthcare technology and services company that provides data, analytics, consulting, and platform-based offerings to biopharma companies with a focus on rare and specialty diseases. Our client's capabilities include the areas of Strategy, Analytics, Digital Transformation, and Patient Identification.

Overview

Our client is on a mission to transform the ecosystem for rare and specialty diseases. They are looking for people who excel in their work and are able to manage ambiguity with resourcefulness and creativity. Their core values are:

• 1. Set high standards
• 2. Deliver on ambitious goals
• 3. Be relentless and innovative
• 4. Be responsible and trusted

Position Summary

We are seeking an experienced Team Lead to oversee and manage a team of data professionals supporting internal data management and operations, and to provide strategic analytics support for biopharmaceutical clients. The ideal candidate will have deep expertise in leveraging commercial data, analytics, and AI to drive business insights and support critical functions across analytics, forecasting, new product development, marketing, sales, market access, patient services, and medical operations.

Key Responsibilities

Overall Team Leadership

• Provide leadership and direction to a team of Junior Data Scientists, Data Engineers, and AI Developers
• Oversee all aspects of the team's work including Dynamic LeadGen (DLG) Operations: DLG is an AI/ML-powered solution that transforms healthcare provider targeting in rare and specialty diseases by using predictive modeling, algorithmic triggers, and omnichannel activation to identify and engage practices and physicians often missed by traditional approaches., Analytics Services, AI Solution & Application Development, and Data Management & Operations
• Ensure quality and timely delivery of all team outputs
• Serve as the primary point of contact between the team and the client
• Manage team performance, development, and growth

Qualifications:

Education: BS in Engineering, Data Science or related degree, MS or higher preferred.

Technical Skills & Experience:

• 5+ years in commercial analytics within the pharmaceutical or biopharmaceutical industry, with hands-on experience in the key areas of Analytics Services and data types
• 2+ years supervising teams of data science and development professionals
• Proficiency in data analytics tools (e.g., SQL, Python, R) and visualization software (e.g., Tableau, Power BI, our tech stack Dashboard, Google Analytics)
• ML/AI expertise and experience are a plus

Behavioral Skills

• Maintain at least three hours of overlapping working hours with the U.S. West Coast (Pacific Time Zone), either in the AM or PM IST
• Ability to recognize trends in complex data
• Excellent communication and collaboration skills
• Ability to thrive in a fast-paced, entrepreneurial, and dynamic startup environment

Required Data Source Expertise:

• Ex-factory Data: Utilize production and distribution data for demand planning and performance analysis
• Outlet/Ship-to Level Sell-in Data: Analyze supply chain data to track inventory and sales flow
• Basic Financial Data: Incorporate financial metrics into commercial analytics
• HCP-level Dispense/Rx Data: Analyze healthcare professional (HCP) prescribing behaviors and trends
• Specialty Pharmacy Data: Leverage specialty pharmacy data to measure product performance and patient outcomes
• Hub/Patient Services Data: Evaluate patient support and adherence programs
• HCP-to-HCO Affiliation Data: Map HCP-to-HCO (healthcare organization) relationships to optimize targeting
• HCO-to-HCO Hierarchy Data: Understand healthcare organization hierarchies for segmentation and sales strategies
• Formulary Data: Analyze formulary status and market access impacts
• Digital Marketing Data: Measure the effectiveness of digital marketing campaigns
• Open/Closed Patient-Level Claims Data: Conduct analytics on claims data to support market access and pull-through strategies as well as understanding treatment flows and patient dynamics
• Chargeback Data: Integrate chargeback analytics into payer and channel strategy assessments