Job descriptionAbout Business
JOB DESCRIPTION
Adani Group: Adani Group is a diversified organisation in India comprising 10 publicly traded companies. It has created a world-class logistics and utility infrastructure portfolio that has a pan-India presence. Adani Group is headquartered in Ahmedabad, in the state of Gujarat, India. Over the years, Adani Group has positioned itself to be the market leader in its logistics and energy businesses focusing on large-scale infrastructure development in India with O & M practices benchmarked to global standards. With four IG-rated businesses, it is the only Infrastructure Investment Grade issuer in India.
Job Purpose: The Predictive Analytics Data Scientist is responsible for designing, building, and optimizing AI-driven predictive models that enhance business intelligence, risk management, and operational efficiency. This role will focus on end-to-end execution, from data collection and model development to deployment and monitoring, ensuring AI models drive actionable insights for demand forecasting, process automation, and business optimization.
Responsibilities
Predictive Analytics Data Scientist-AI Labs
Predictive Model Development & Optimization
Develop and train predictive models using time-series forecasting, regression analysis, and anomaly detection to solve business challenges.
Optimize predictive accuracy by implementing feature engineering, hyperparameter tuning, and deep learning techniques.
Test, refine, and validate AI models through rigorous model evaluation, stress testing, and bias mitigation to ensure reliability.
Ensure real-time model scalability by deploying on cloud-based AI infrastructure (AWS, GCP, Azure) using Kubernetes, Docker, and MLOps frameworks.
Business Problem Solving & AI Integration
Understand and translate business challenges into AI-driven solutions, ensuring alignment with strategic objectives.
Implement AI-powered demand-supply forecasting models to enhance production planning, logistics, and inventory management.
Enhance operational efficiency by integrating predictive analytics into decision workflows, process automation, and real-time optimization.
Improve cost management and revenue forecasting by leveraging AI models for risk assessment, fraud detection, and business performance monitoring.
Data Collection, Feature Engineering & Model Training
Manage structured and unstructured data pipelines, ensuring high-quality inputs for predictive modeling.
Conduct feature extraction and engineering to optimize data representation and improve model performance.
Automate model training pipelines to enhance scalability, version control, and real-time learning capabilities.
Model Deployment, Monitoring & Performance Evaluation
Deploy AI models into production, ensuring seamless integration into enterprise applications and business workflows.
Monitor model performance in real-world scenarios, identifying and addressing model drift, bias, and performance degradation.
Continuously improve model efficiency through iterative learning, retraining cycles, and adaptive optimization techniques.
Collaboration With AI, Engineering & Business Teams
Work closely with model deployment teams to ensure AI solutions are effectively operationalized.
Coordinate with front-end developers to integrate predictive insights into AI-powered dashboards and user applications.
Partner with business teams to align AI models with key performance indicators (KPIs) and measurable business impact.
AI Governance, Compliance & Risk Management
Ensure AI compliance with industry regulations, data privacy laws (GDPR, AI Act), and company-wide governance frameworks.
Implement explainability frameworks to improve transparency and interpretability of predictive models.
Conduct risk assessments to identify and mitigate model bias, ethical concerns, and unintended consequences.
Key Stakeholders - Internal
AI & Data Science Teams
Business Leaders & Strategy Teams
IT & Cloud Engineering Teams
Finance & Risk Management Teams
Key Stakeholders - External
Technology Partners & AI Vendors
Research Institutions & Academia
Regulatory Bodies
Qualifications
Educational Qualification:
Master's degree in Computer Science, Data Science, Statistics, Mathematics, Artificial Intelligence, or related fields.
Certification
Certified Machine Learning Engineer (Google AWS Microsoft AI Certification)
Professional Certification in Predictive Analytics (Coursera IBM DataCamp)
MLOps & Cloud AI Certification (AWS GCP Azure)
Skills Required
Docker, Predictive Modeling, Deep Learning, Kubernetes, Regression Analysis, Gcp, MLops, Risk Assessment, anomaly detection, Azure, Aws
