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
Formulate a comprehensive AI and analytics roadmap aligned with the organizations goals, focusing on improving operational efficiency.
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.
Identify cross-functional use cases, such as using AI to predict market demand, optimize pricing strategies, or enhance employee training programs.
Apply AI for predictive maintenance of equipment, and process optimization while using BI to monitor production KPIs and identify bottlenecks through historical data analysis.
Stakeholder Engagement - Collaborate with executive leadership, functional heads, and IT to identify analytics needs, Translate business questions into actionable insights and dashboards
Leadership: Lead the Analytics and AI team, provide strategic insights to the C-suite, and foster a data-driven culture.
Data-Driven Decision Support - Deliver KPIs, scorecards, and predictive models to enable strategic decision-making, Promote advanced analytics, AI/ML initiatives, and scenario planning
Qualification:
Bachelors or masters in computer science, Data Science, Statistics, or related field. PhD is a plus.
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
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).