Predictive Analytics Data Scientist-AI Labs

Responsibilities

Key Responsibilities

• Develop and deploy predictive analytics models to optimize operations in energy, logistics, and infrastructure sectors.
• Collaborate with cross-functional teams to identify key business challenges and design AI-driven solutions.
• Utilize machine learning algorithms to forecast asset performance and enable predictive maintenance.
• Analyze large datasets to uncover actionable insights that enhance decision-making processes.
• Design and implement AI models for supply chain optimization and resource management.
• Contribute to the development of proprietary AI algorithms tailored to Adani Group’s operational needs.
• Monitor and evaluate the performance of predictive models, ensuring accuracy and reliability.
• Stay updated with the latest advancements in AI and machine learning to integrate cutting-edge technologies into projects.
• Support the group’s sustainability initiatives by developing models to monitor and reduce carbon footprints.
• Document and present findings, methodologies, and results to stakeholders in a clear and actionable manner.
  

Qualifications

Required Qualifications & Experience

• Proven experience (1-3 years) in predictive analytics, machine learning, or data science roles.
• Hands-on experience with Python, R, or similar programming languages for data analysis and model development.
• Strong understanding of machine learning algorithms, statistical methods, and data visualization techniques.
• Experience in working with large datasets and cloud-based platforms (e.g., AWS, Azure, or Google Cloud).
  

Educational Background

• Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, or a related field.
• Advanced certifications in AI/ML or predictive analytics are preferred.
  

Years & Type Of Experience

• 1-3 years of experience in predictive analytics, preferably within industries such as energy, logistics, or infrastructure.
• Demonstrated ability to develop and deploy machine learning models in real-world scenarios.