Associate Director - Customer Behaviour Analytics

Merck; Pune District; On-site; INR 4,500,000 - 7,500,000; Full time

Job summary

Merck in Pune, India seeks Associate Director - Customer Behaviour Analytics to lead the analytics that define who matters across CK&M and to drive activation and measurement. You will guide a 15+ person team across segmentation, modelling, ICP/KOL builds, content affinity, and data-spine enhancements, collaborating with DHH, IT, and Field & CRM.

The role emphasizes strategic analytics in pharma/biotech, strong leadership in ambiguity, and ability to translate business questions into

Responsibilities

• Lead customer model-build efforts including segmentation, profiling, propensity and risk-stratification models, and early adopter/follower analyses.
• Own analytics for ICP and KOL identification; define inputs, logic, refresh cadence and validation criteria.
• Coordinate across PIE phases and data augmentation (EMR, Rx, etc.) and consumer extensions where applicable.
• Drive Content Affinity analytics to tailor recommendations and quantify resonances across segments.
• Lead and mentor teams, manage workload, and orchestrate cross-functional insights pods.
• Prioritize work with BU LT, articulate value and impact, and track realized business value.
• Engage stakeholders across DHH, Data Strategy & Solutions, IT, and Field & CRM for aligned outcomes.
• Pioneer innovation and GenAI experimentation, contributing to scalable data solutions.

Job description

Job Description

Associate Director - Customer Behaviour Analytics

The Opportunity This role sits within the Customer Knowledge & Measurement (CK&M) vertical of Digital Human Health (DHH). The Associate Director, Customer Behavior Analytics owns the \"know the customer\" of CK&M: the model-build and insight-generation layer that produces the customer understanding the rest of CK&M (Field & CRM Engagement) acts on and measures. The Associate Director will be responsible for leading the analytics that define who matter and why: HCP / account / consumer segmentation, micro-segmentation, propensity and risk-stratification modelling, integrated customer profiling, KOL identification, content-affinity modelling, campaign analytics and the data-product spine that powers downstream activation. The team supports the US Commercial Organization, informing customer engagement strategy. The role is a key node in evolving the India Hub from delivery support to strategic analytics partner across the customer-insight value chain.

Role Overview The Associate Director will lead a team of 15+ covering capability domains under Customer Behavior Analytics. The candidate must be able to lead direct teams and orchestrate cross-functional insights pods spanning DHH, Data Strategy & Solutions, IT and the broader DHH organization. The individual will demonstrate consistently strong leadership, the ability to work objectively in ambiguity, strong partnership skills, and a culture of continual learning. The preferred candidate will have an entrepreneurial spirit, a consultative and strategic mindset, deep customer-analytics experience in pharma / biotech (segmentation, micro-segmentation, campaign analytics, productized customer-data offerings), fluency in technology (AI, agentic, GenAI), and a demonstrated record of producing actionable insights from analytics. The individual should have a strong understanding of the US data landscape (claims, EMR / EHR, Rx, activity, promotional data, content-engagement data) and the ability to translate ambiguous business questions into advanced, scalable analytical solutions.

What will you do in this role:

• Customer model-build : Lead the development of customer-understanding models that drive downstream activation and measurement: HCP segmentation (personal / non-personal), account / IDN segmentation, patient segmentation and profiling, propensity models (writer / non-writer), patient risk-stratification (initial model build with analog / business rules, progressing to ML), and early adopter / follower analysis.
• Integrated Customer Profile (ICP) and KOL build: Own the analytics build for ICP and the KOL-identification capability. Define data inputs, model logic, refresh cadence and validation criteria.
• Patient Insights Engine (PIE) and consumer extensions: Lead the build and evolution of the customer-data spine across PIE phases (CDM, Business Rules, Data Augmentation including EMR, and Predictive Models) and the consumer extension (PIECON) where applicable.
• Content Affinity: Drive micro-segmentation analytics to enable targeted content recommendations and quantify which content resonates with distinct segments, informing both brand strategy and Field & CRM execution.
• Team leadership and cross-functional orchestration: Lead and mentor Senior Managers (and the specialists / interns reporting into them), providing guidance on best practices in data analytics, modelling, validation and insight synthesis. Manage team workload proactively and ensure optimal resource utilization. Lead cross-functional insights pods in addition to direct teams.
• Prioritization and value tracking: Understand the full book of work underway and planned for each disease area or business unit. Work with BU LT to prioritize against the team's capacity. For prioritized work, engage cross-functional teams to articulate the value and impact of each piece. Encourage the mindset to articulate the \"so what\" and \"now what\" and to track realized business value.
• Stakeholder collaboration: Drive proactive, transparent engagement with cross-functional partners to co-create solutions, align on organizational priorities, and set clear expectations. Provide internal and business stakeholders with forward-looking visibility into key programs to inform milestones, prioritization, risk mitigation, and aligned decision-making.
• Pharma data-ecosystem analytics: Analyze data across disparate data sources (claims, EMR, Rx, activity, promotional, consumer, content) to develop insights that inform commercial business strategies.
• Advanced analytics and modelling : Leverage advanced statistical methods and ML/AI modelling with strong validation practices; deliver regression and time-series models, and segmentation/clustering approaches (k-means, hierarchical) to generate actionable insights.
• Innovation and GenAI experimentation : Contribute to and lead innovation experiments (idea generation, idea incubation, experimentation), including responsible app