DGM - Analytics and Master Data (GCC)

About Alkem:

Alkem Laboratories Limited is an Indian multinational pharmaceutical company headquartered in Mumbai, that manufactures and sells pharmaceutical generics, formulations and nutraceuticals in India and globally over 50 countries. We have consistently been ranked amongst the top five pharmaceutical companies in India. Our portfolio includes illustrious brands like Clavam, Pan, Pan-D, and Taxim-O, which feature amongst the top 50 pharmaceutical brands in India.

About Alkem GCC:

Alkem's Global Capability Centre in Airoli, Navi Mumbai is the company's transformation engine. The GCC consolidates and runs global processes across HR Operations, IT, Sales Enablement, Procurement and Master Data for Alkem's domestic and international entities, with Finance and Supply Chain on the transition roadmap. The mandate is AI-led automation, international process transition and capability building.

Role purpose:

Every automation and every dashboard in the GCC sits on master data. This role owns the data the transformation runs on: clean, governed and single-source, across customer, vendor, material, employee and doctor domains. The scope covers all master data and analytics solutions in the GCC, including the data layer beneath commercial intelligence. The role runs a six member master data operations team alongside the analytics, and holds the data readiness gate that every automation must pass.

Key responsibilities:

1.Master Data Governance

• Establish and own the master data governance framework: data ownership model, definitions, creation and change workflow, approval gates and retirement rules.
• Define the golden source for each domain and eliminate competing sources of record.
• Harmonize master data across SAP S/4HANA, SAP Ariba, SuccessFactors, GARNET and downstream systems.
• Maintain the GCC data dictionary and the definition of every published metric.

2.Data Quality and Identity Resolution

• Publish a master data quality scorecard per domain covering completeness, validity, uniqueness, consistency and timeliness, with remediation owners and dates.
• Own identity resolution and de-duplication at scale. Design matching architecture that requires agreement across independent fields before any merge, and measure false merge rate rather than assuming it.
• Run field confirmation and verification cells where source data cannot be resolved by system evidence alone.

3.Analytics and Reporting

• Own the GCC analytics as a single source of truth: operational dashboards, executive scorecards, SLA tracking and functional reporting.
• Identify, design and drive predictive analytics solutions and dashboards for the GCC.
• Deliver the reporting for the GCC metric set.
• Own the data layer beneath commercial intelligence and doctor-level analytics, including the historical data sets used to prove capability before external spend.
• Establish self-service analytics so that routine reporting does not depend on this team.
• Apply advanced techniques where they earn their place: anomaly detection, forecasting, process mining and automated insight generation.

4.Master Data Operations

• Lead the six member master data team running creation, change, validation and cleansing across domains.
• Set service levels, quality checks and productivity standards for master data transactions, and report against them.
• Drive the automation of master data operations in partnership with the AI and automation teams, and hold the team's transition from execution to exception handling.

5.Data Readiness for Automation

• Hold the data readiness gate. No automation ships against an ungoverned field, an undefined value set or an unmeasured quality level.
• Provide the labeled historical data sets that AI solutions are evaluated against.
• Advise solution design on data structure, integration pattern and reconciliation.

6.Privacy, Retention and Control

• Define and maintain data handling standards across jurisdictions, including retention, minimization and access control, in coordination with legal and compliance.
• Own access governance for master data and analytics assets.
• Produce evidence for internal and statutory audit and close data-related findings to agreed dates.

Candidate profile:

Experience

• 12 to 16 years, with at least five years in master data management, data governance or data quality in a multi-entity environment.
• Analytics delivery experience with named products in production use, not only oversight of a reporting team.
• Experience leading a transactional data operations team alongside a governance and analytics agenda.
• Pharmaceutical customer, doctor or field force data exposure is a strong advantage.

Technical Skills

• Power BI at expert level: data modeling, DAX, Power Query, gateway and workspace governance.
• SQL at working depth. Python for data preparation, matching and analysis is a strong advantage.
• Master data management tooling and governance frameworks.
• Source system knowledge across SAP S/4HANA, SAP Ariba and SuccessFactors, including extract structure and reconciliation.
• Record linkage and fuzzy matching techniques, clustering, and quality measurement methodology.
• Awareness of data protection obligations including GDPR and India's data protection regime.

Education and Certifications

• Bachelor's degree in Engineering, Computer Science, Statistics or Commerce. Postgraduate qualification in analytics, statistics or management preferred.
• Certifications in Power BI, data governance or SAP master data are an advantage.