Manager - Advance Analytics

Manager - Advance Analytics

Lo que hacemos

En Kenvue, nos damos cuenta del extraordinario poder del cuidado diario. Sobre la base de más de un siglo de herencia y arraigados en la ciencia, somos el hogar de marcas icónicas, incluidas NEUTROGENA®, AVEENO,® TYLENOL,® LISTERINE,® JOHNSON'S® y BAND-AID® que ya conoces y amas. La ciencia es nuestra pasión; El cuidado es nuestro talento.

Quiénes somos

Nuestro equipo global está formado por ~ 22.000 personas brillantes con una cultura laboral en la que cada voz importa y cada contribución es apreciada. Nos apasionan las ideas, innovación y comprometidos con la entrega de los mejores productos a nuestros clientes. Con experiencia y empatía, ser un Kenvuer significa tener el poder de impactar a millones de personas todos los días. Ponemos a las personas en primer lugar, nos preocupamos ferozmente, nos ganamos la confianza de la ciencia y resolvemos con coraje, ¡y tenemos oportunidades brillantes esperándote! Únase a nosotros para dar forma a nuestro futuro y al suyo. Para obtener más información, haga clic en aquí.

Role reports to:

Senior Manager - Forecasting & Analytics

Ubicación:

Asia Pacific, India, Karnataka, Bangalore

Lugar de trabajo:

Híbrido

What will you

Role Summary

The Manager – Advanced Analytics will lead the design, development, and scaling of advanced analytics solutions that drive enterprise decision-making across forecasting, machine learning, and agentic AI use cases within the broader analytics and decision intelligence ecosystem.

This role requires strong hands-on experience in advanced forecasting, machine learning, and modern AI approaches including agentic AI. The manager will be accountable for translating business problems into scalable analytical products, taking solutions from problem framing and model design → MVP → production deployment, while driving measurable business impact through predictive, prescriptive, and intelligent automation capabilities.

Key Responsibilities

Advanced Forecasting, ML & Agentic AI

• Lead the design and deployment of advanced forecasting solutions across demand, supply, commercial, or operational use cases using statistical, machine learning, and hybrid modeling approaches.
• Build and productionize machine learning models for prediction, classification, segmentation, anomaly detection, and decision support.
• Drive the adoption of agentic AI capabilities to automate insight generation, exception triaging, scenario evaluation, and decision workflows.
• Define fit-for-purpose approaches that combine forecasting, ML, optimization, and GenAI/agentic patterns based on business value, scalability, and explainability.
• Apply human-in-the-loop, governance, and monitoring practices to ensure reliability, auditability, and responsible AI usage.

Advanced Analytics & Modelling Foundations

• Develop and review advanced forecasting, predictive, and causal models using time-series methods, machine learning, and deep learning techniques.
• Establish robust practices for feature engineering, model evaluation, backtesting, accuracy measurement, and model monitoring.
• Translate complex analytical outputs into business recommendations and executive-ready narratives that support decision-making.

Architecture, Platform & Deployment

• Deploy and operationalize forecasting, ML, and agentic AI solutions on enterprise cloud platforms such as Azure, including model training, versioning, deployment, and monitoring.
• Integrate analytical solutions with enterprise data platforms, business applications, and decision workflows.
• Define reusable modeling, deployment, and orchestration standards to improve scalability and speed to value.
• Ensure solutions meet enterprise expectations for security, reliability, scalability, and Responsible AI compliance

Delivery & Stakeholder Leadership

• Own delivery of advanced analytics initiatives from use-case identification and experimentation → MVP → production.
• Partner with Product, IT, Data Engineering, and Business leaders to identify high-impact opportunities and drive adoption.
• Translate analytical and AI outputs into clear business actions, value stories, and executive-ready recommendations.

People & Capability Leadership

• Lead, mentor, and grow a team of analysts, data scientists, and ML practitioners.
• Build deep capability in advanced forecasting, machine learning, experimentation, and agentic AI.
• Drive technical design reviews, best practices, and cross-team knowledge sharing.

Required Qualifications (Must‑Have)

• Bachelor’s or Master’s degree in Statistics, Mathematics, Computer Science, Engineering, Economics, Operations Research, or a related quantitative field.
• 8–10 years of experience in advanced analytics, data science, forecasting, or machine learning, with a strong record of production deployment.
• Hands-on experience in advanced forecasting techniques, including statistical, machine learning, and hybrid forecasting methods.
• Strong expertise in machine learning, model evaluation, experimentation, and analytical problem solving.
• Exposure to or hands-on experience with agentic AI / GenAI-enabled analytical workflows, such as intelligent automation, insight generation, or decision support agents.
• Proficiency in Python and SQL, with experience building production-grade analytics solutions.
• Experience deploying analytics or AI solutions on Azure or comparable cloud platforms.
• Proven experience leading teams and influencing cross-functional and senior stakeholders.

Preferred (Still Valuable, Not Mandatory)

• Experience with demand forecasting, supply chain analytics, commercial analytics, or decision intelligence platforms.
• Exposure to optimization, causal AI, scenario modeling, or simulation-based decisioning.
• Familiarity with MLOps, Responsible AI, risk controls, and governance for AI/GenAI systems.
• Experience working in consumer goods, supply chain, operations, finance, or commercial analytics domains.

Success Measures

• Advanced analytics solutions deployed in production with measurable business adoption and value realization.
• Improved forecast accuracy, decision quality, and speed to insight across key business workflows.
• Scalable use of ML and agentic AI capabilities across multiple business use cases.
• Strong uplift in team capability across forecasting, advanced analytics, and intelligent automation.

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