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Job Description

Join Our Team at Zydus Takeda Healthcare Private Limited at Navi Mumbai

At Zydus Takeda excellence drives us and innovation inspires us. Be part of a dynamic environment where your expertise ensures seamless operations and your passion makes a difference in transforming lives. Step into a rewarding career and contribute to delivering life-changing healthcare!

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Designation : Manager Manufacturing

Qualification: - Chemical Engineering preferably from reputed institutes (ICT /IIT)

Experience: Minimum 8 to10 years in API manufacturing Plant

The incumbent will be a hands-on technical leader to drive data-enabled process understanding and control in GMP manufacturing. This role bridges process engineering and advanced analytics (MVDA/AI/ML) to improve yield cycle time robustness and deviation prevention while supporting the sites roadmap on digital manufacturing PAT/monitoring strategy and continuous manufacturing (flow/process intensification).

Success requires practical understanding of unit operations and shopfloor realities and the ability to translate analytics into standard work control strategy and daily management routines.

Key Responsibilities:

Process Monitoring Variability Reduction and MVDA (SIMCA)

• Lead MVDA workstreams using SIMCA (or equivalent): PCA/PLS modelling batch variability analysis golden batch definition multivariate monitoring limits and root-cause troubleshooting.
• Establish multivariate univariate monitoring strategies (SPC/PAT where applicable) and support investigations for excursions and deviations.
• Convert insights into actionable operating recommendations and embed them into Tier 1/2/3 daily management (triggers actions escalation countermeasure tracking).

AI/ML Use Cases for Manufacturing (GMP-Appropriate)

• Identify and execute AI/ML applications such as anomaly detection early warning for deviations batch quality prediction yield-loss drivers downtime and predictive maintenance.
• Own the lifecycle: problem framing data readiness modeling validation deployment support monitoring and re-training triggers.
• Ensure governance and compliance: documentation auditability version control and data integrity practices aligned with GMP/ALCOA.

Digital Twin / Modelling / Simulation Enablement

• Develop and deploy fit-for-purpose models (first principles hybrid data-driven) for critical unit operations to accelerate optimization and scale-up.
• Drive DoE-based process characterization and model validation/verification (design execution support analysis and knowledge capture).
• Partner with global/centre teams to develop digital twin components for prioritized processes/unit operations.

Data Foundations and Pipeline Enablement (Plant-to-Insights)

• Create and maintain practical data pipelines from plant and quality systems (DCS/PLC historians LIMS MES SAP/ERP CMMS) to analytics environments.
• Improve data contextualization quality checks and standard definitions (tags batch mapping timestamp alignment missing data strategies).
• Build reusable analysis templates dashboards and storyboards to support operations and leadership decisions.

Continuous Manufacturing / Flow Chemistry Support (Feasibility Scale-up)

• Support process screening for flow/continuous suitability: mixing heat/mass transfer RTD pressure drop catalyst performance plugging risks scalability.
• Define DoE-based development plans for continuous processes and document learnings for tech transfer readiness.
• Collaborate with MSci/Engineering/EHS for hazard assessment and safe-by-design controls for intensified operations (exotherms runaway prevention relief strategy inputs).

Capability Building and Cross-Functional Leadership

• Coach teams on statistics MVDA DoE and problem solving to build sustainable capability.
• Participate in cross-site analytics communities and share best practices.

Qualifications (Desirable)

• Education: M.E./ in Chemical Engineering (or equivalent).
• Experience: 810 years in API or Formulations manufacturing / MSAT / MSci / process engineering roles in regulated environments.
• Proven track record converting analysis into measurable manufacturing improvements (quality yield cycle time reliability deviations).

Technical Skills (Must-Have)

• MVDA: SIMCA (preferred) or equivalent; PCA/PLS; interpretation of multivariate control and batch variability.
• Statistics: SPC capability regression hypothesis testing and DoE (design analysis translation to actions).
• Analytics: Python or R (preferred) for reproducible analysis; data wrangling and time-series handling; visualization/storyboarding.
• Manufacturing systems familiarity: historians/time-series data linkage with LIMS/MES/SAP/CMMS; strong data contextualization ability.
• GMP & Data Integrity: ALCOA mindset; documentation discipline; understanding what requires change control/validation support.

Desirable (Nice-to-Have / Differentiators)

• Process modelling/kinetics tools: gPROMS DynoChem ReactionLab (or equivalent).
• CFD exposure: Ansys / STAR-CCM (or equivalent).
• Manufacturing analytics platforms: BioVia Discoverant PI System analytics advanced BI tooling.
• Flow chemistry/continuous manufacturing exposure (PFR/CSTR/packed-bed) and practical scale-up risk thinking.
• Low-code apps/dashboards (PowerApps/Power BI) used under appropriate governance.

Behavioural Competencies

• Human-centric problem solving: designs solutions that simplify work and improve decision quality on the shopfloor.
• Systems thinking: links process physics data signals and business outcomes.
• Resilience mindset: builds monitoring and early-warning systems to prevent disruptions and support reliable supply.
• Influence without authority: can drive cross-functional adoption and standardization.
• High ownership structured thinking and the ability to manage multiple priorities independently.

What You Will Get to Do

• Build a modern resilient manufacturing capability combining process engineering digital analytics.
• Lead high-impact projects with direct visibility to site leadership and global teams.

Work on advanced manufacturing themes including MVDA-led control strategies digital twins and continuous manufacturing enablement.

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