Head- Business Intelligence & AI

Role Title:

Reporting To:

Location of Posting:

Position Overview:

Head- Business Intelligence & AI

As the Head of AI and Analytics in a chemical manufacturing organization, your role involves leveraging AI and analytics across all functions—R&D, production, supply chain, sales, marketing, finance, HR, and compliance—while incorporating dashboarding, ETL processes, and a data lake to enable data-driven decision-making.

Key Responsibilities:

1. Data Strategy Leadership - Define and drive the enterprise-wide business intelligence and analytics strategy , Align BI initiatives with overall business goals and digital transformation priorities
2. Formulate a comprehensive AI and analytics roadmap aligned with the organization’s goals, focusing on improving operational efficiency.
3. Oversee the design and maintenance of a centralized data lake to store diverse data, ensuring scalability, security, and accessibility for cross-functional BI and AI initiatives.
4. Identify cross-functional use cases, such as using AI to predict market demand, optimize pricing strategies, or enhance employee training programs.
5. Apply AI for predictive maintenance of equipment and process optimization while using BI to monitor production KPIs and identify bottlenecks through historical data analysis.
6. Stakeholder Engagement - Collaborate with executive leadership, functional heads, and IT to identify analytics needs, Translate business questions into actionable insights and dashboards
7. Leadership: Lead the Analytics and AI team, provide strategic insights to the C-suite, and foster a data-driven culture.
8. Develop and maintain interactive dashboards for all functions, providing real-time insights to stakeholders
9. Data-Driven Decision Support - Deliver KPIs, scorecards, and predictive models to enable strategic decision-making, Promote advanced analytics, AI/ML initiatives, and scenario planning

AI & GenAI Enablement:

Spearhead AI and Generative AI initiatives, including hands-on leadership in deploying LLMs, implementing RAG (Retrieval-Augmented Generation) models, and identifying data science-driven opportunities across the organization.

Data Governance & Quality:

Ensure best practices in data governance, security, and quality management to uphold data integrity and compliance.

Education Qualification:

• Bachelor’s or master’s in computer science, Data Science, Statistics, or related field. PhD is a plus.

Experience:

• 10+ years of experience in analytics, data architecture, or related roles.
• Strong knowledge of data modelling techniques
• Understanding of Data Science (SQL, Python, R, and at least one cloud platform.
• Experience with modern data warehousing tools (Snowflake, BigQuery, Redshift) and orchestration (Airflow, DBT)

Technical Competencies/Skills:

• Analytics tools (Data Lake, Tableau), and integration with other systems
• Deep understanding of manufacturing processes and best practices.
• Proven track record of implementing enterprise analytics solutions and predictive modeling at scale.
• Strong hands-on experience with tools like Power BI, Tableau, Python/R, SQL, and cloud platforms (AWS/GCP/Azure) or any other relevant cloud platform.
• Experience setting up and managing

  data lakes

  and developing end-to-end data pipelines.
• Sound understanding of

  AI/ML techniques

  ,

  LLMs

  ,

  GenAI tools

  , and emerging technologies in data science.
• Experience with modern data warehousing tools (Snowflake, BigQuery, Redshift) and orchestration (Airflow, DBT).

Behavioural Competencies:

• Strong leadership and team management skills.
• Excellent communication and interpersonal skills.
• High level of initiative and proactive approach to problem-solving.
• Ability to work under pressure and manage multiple priorities.
• Excellent verbal and written communication skills, with the ability to present complex information to both technical and non-technical stakeholders.
• Strong analytical and problem-solving skills, with the ability to make data-driven decisions.