Manager Manufacturing Analytics

Location - Hyderabad

Industry - Pharmaceutical

KEY RESPONSIBILITIES

1. Apply statistical, machine learning, and data science methods to solve complex business and scientific problems.
2. Lead development and deployment of predictive models and multivariate analytics for process monitoring, anomaly detection, and performance optimization.
3. Collaborate with cross-functional teams (manufacturing, quality, IT) to identify, design, and implement data-driven solutions that drive measurable value. Partner with technical teams to resolve data gaps and inconsistencies, and support validation activities.
4. Translate business problems into analytics approaches, define success metrics, and communicate recommendations to technical and non-technical audiences. Drive exploratory analysis, trend analysis, forecasting, and modeling using multiple data sources.
5. Contribute to the development and deployment of AI-enabled solutions, including machine learning and generative AI use cases where appropriate. Facilitate knowledge transfer, training, and adoption of solutions across global teams.
6. Work with IT teams to deliver secure, scalable analytics products (dashboards, data products, model services) and manage project interdependencies.
7. Integrate and transform data from Azure Data Lake (ADLS), Databricks, enterprise applications, and other data sources to create scalable reporting and analytics solutions. Leverage Power BI and SEEQ for actionable insights.
8. Support integration and harmonization of data across Historian, MES, LIMS, ERP, and analytics platforms.
9. Ensure data quality, integrity, and compliance with GMP and regulatory standards. Maintain documentation, best practices, and knowledge repositories for analytics solutions.
10. Track and report analytics project progress, risks, and outcomes to leadership and stakeholders. Monitor adoption KPIs, gather user feedback, and drive continuous improvement.

EDUCATIONAL QUALIFICATION

• Bachelors degree in Data Analytics, Computer Science, Information Systems, Engineering, Statistics, Business Analytics, or a related field
• 5–8 years of experience in data science, analytics, or digital deployment within the pharmaceutical or batch process manufacturing industry

CERTIFICATIONS PREFERRED

• SEEQ and OSIsoft PI (AVEVA) certification/training
• Microsoft Fabric, Power BI, Power Platform
• Databricks

TECHNICAL SKILLS REQUIRED

• Expert level in time series analytics: SEEQ, AVEVA (OSIsoft PI) Vision
• Expert level in Business Intelligence: MS Fabric (Power BI, Power Automate, Power Apps)
• Familiarity with enterprise applications: Data Historian, MES, LIMS, SAP, QMS
• Familiarity with project management tools: JIRA or similar

EXPERIENCE REQUIRED

Domain Experience

• Strong understanding of pharma manufacturing unit operations and process monitoring concepts. Experience with Continued Process Verification (CPV) and Annual Product Quality Review (APR/PQR) documentation is an advantage.
• Domain knowledge of GxP regulations, audits, and data integrity concepts.
• Understanding of SDLC stages, environments, test scripts, documentation, and validation.

Descriptive Analytics

• Hands-on experience building Power BI dashboards and reports: connecting to data sources, creating measures and table relationships.
• Data modeling experience with star and snowflake schemas (DIM and FACT tables).
• Working experience with IDEs such as VS Code.
• Strong SQL skills, including various types of joins.

Diagnostic and Predictive Analytics

• Expertise in multivariate statistical models (e.g., PCA/PLS) and predictive models for near real-time process monitoring, anomaly detection, and performance optimization.
• Experience designing and visualizing time series data on overlay plots using event frames.

Data Connectivity

• Familiarity with cloud analytics (Azure/AWS), data pipelines, and APIs. Experience with NLP for unstructured manufacturing knowledge is preferred.
• Familiarity with machine learning, AI, and generative AI concepts and practical applications.

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