Position:
• Data Scientist -- AI Labs
• Entry level focused data science role within advanced AI research and applied analytics teams
• Role centered on building predictive intelligence systems for large scale business use cases
• Opportunity to work on end to end AI model lifecycle from problem framing to deployment
Company:
• Adani Enterprises Limited
• Flagship organization of the Adani Group
• Large scale Indian enterprise operating across infrastructure and utilities
• Known for long term nation building initiatives and enterprise innovation
Location:
• Ahmedabad
• Gujarat
• India
• Primary work location at Shantigram campus
Job type:
• Full time employment
• Permanent role within core AI Labs team
Job mode:
• Onsite
• Collaboration driven environment within enterprise AI research labs
Job requisition id:
• 44281
Years of experience:
• This Role Is Explicitly for 2025-26 Pass out MTech Candidates Only
• Suitable for freshers and early career professionals based on role scope
Company Description
• Adani Enterprises Limited is the flagship company of one of India’s largest and most diversified business groups
• The organization plays a foundational role in incubating and scaling emerging infrastructure focused businesses
• Over the years the company has successfully built and demerged multiple large listed entities across ports energy transmission logistics and utilities
• The company has played a key role in driving India’s infrastructure growth while aligning with national priorities such as energy security sustainability and self reliance
• Adani Enterprises focuses on identifying high impact sectors and building businesses with long term economic and social value
• Its current strategic investments span airports road infrastructure data centers water management and next generation utilities
• The company operates at massive scale and complexity creating strong demand for advanced analytics artificial intelligence and automation
• AI Labs within the organization function as a central capability to drive data driven decision making across businesses
• Employees gain exposure to real world data problems with direct business outcomes
• The organization emphasizes technology driven growth operational efficiency and enterprise wide innovation
• Strong governance frameworks and compliance driven operations guide AI development practices
• The company offers a structured enterprise environment combined with exposure to cutting edge AI applications
Profile Overview
• This role focuses on designing developing and deploying predictive analytics solutions for enterprise scale problems
• The Predictive Analytics Data Scientist works across the full AI lifecycle starting from understanding business needs to production deployment
• The role involves working with time series data regression models anomaly detection techniques and advanced machine learning approaches
• Professionals in this role contribute to demand forecasting risk analysis operational optimization and performance monitoring
• The position requires translating business challenges into analytical problem statements
• The role emphasizes building models that are not just accurate but also scalable interpretable and reliable in production
• The individual works closely with AI engineers cloud teams and business stakeholders
• Exposure includes real time decision systems automation pipelines and enterprise dashboards
• The role demands a strong foundation in data handling feature engineering and model evaluation
• Continuous learning and iteration form a core part of daily responsibilities
• The role sits at the intersection of data science business strategy and engineering execution
• Professionals are expected to contribute to AI governance explainability and ethical risk mitigation
• This position provides hands on experience with enterprise grade AI infrastructure and cloud platforms
• The work directly impacts cost optimization efficiency improvement and revenue planning
• The role is well suited for candidates looking to build strong applied AI careers in large organizations
Responsibilities
Predictive Model Development and Optimization
• Design and build predictive models to solve real business problems
• Apply time series forecasting methods for demand planning and trend analysis
• Develop regression based models to understand drivers of business performance
• Implement anomaly detection techniques to identify risks and irregular patterns
• Improve model accuracy through advanced feature engineering approaches
• Perform hyperparameter tuning to enhance predictive performance
• Explore deep learning methods where appropriate for complex patterns
• Evaluate models using robust validation techniques
• Conduct stress testing to assess model stability under changing conditions
• Address bias and fairness concerns during model development
• Ensure models meet enterprise reliability standards
• Continuously refine models based on feedback and performance metrics
Business Problem Solving and AI Integration
• Collaborate with business teams to understand operational challenges
• Translate high level business goals into analytical problem definitions
• Design AI driven solutions aligned with strategic objectives
• Develop demand supply forecasting systems for planning use cases
• Support logistics and inventory optimization through predictive insights
• Embed analytics outputs into operational decision workflows
• Enable automation through predictive recommendations
• Support financial planning through revenue and cost forecasting models
• Build risk assessment models for operational and financial domains
• Contribute to fraud detection and performance monitoring initiatives
• Ensure AI outputs are actionable and decision focused
Data Collection Feature Engineering and Model Training
• Manage structured and unstructured data from multiple enterprise sources
• Ensure data quality consistency and reliability across pipelines
• Perform feature extraction to improve model learning efficiency
• Engineer meaningful variables that capture business signals
• Automate data preprocessing workflows for scalability
• Build reusable model training pipelines
• Enable version control and reproducibility of experiments
• Support real time and batch training scenarios
• Implement monitoring hooks during training for early issue detection
• Maintain documentation for datasets and features
Model Deployment Monitoring and Performance Evaluation
• Deploy trained models into production environments
• Integrate AI outputs into enterprise applications and systems
• Work with engineering teams to ensure smooth deployment workflows
• Monitor live model performance and prediction quality
• Detect model drift caused by data or behavior changes
• Identify bias or degradation in real world usage
• Trigger retraining cycles based on performance thresholds
• Optimize inference efficiency and response times
• Support adaptive learning strategies for evolving data
• Maintain logs and metrics for governance and audits
Collaboration with AI Engineering and Business Teams
• Work closely with AI engineers during deployment phases
• Coordinate with cloud teams on infrastructure requirements
• Collaborate with frontend teams to surface insights through dashboards
• Support visualization of predictions and trends
• Align model metrics with defined business KPIs
• Participate in cross functional reviews and planning sessions
• Communicate findings clearly to non technical stakeholders
• Contribute to enterprise wide AI initiatives
• Support pilot programs and scaled rollouts
• Act as a bridge between data science and business teams
AI Governance Compliance and Risk Management
• Ensure models comply with internal governance frameworks
• Follow data privacy regulations applicable to enterprise data
• Support explainability and interpretability of predictive models
• Implement documentation for model assumptions and limitations
• Conduct risk assessments related to bias and ethics
• Identify unintended consequences of model usage
• Support audit readiness for AI systems
• Promote responsible AI practices within teams
• Contribute to continuous improvement of governance standards
Key Stakeholders Internal
• AI and Data Science teams
• Business leadership and strategy teams
• IT and cloud engineering teams
• Finance and risk management teams
Key Stakeholders External
• Technology partners and AI vendors
• Research institutions and academic collaborators
• Regulatory and compliance bodies
Qualifications
Educational Qualification
• Master’s degree in Computer Science
• Master’s degree in Data Science
• Master’s degree in Statistics
• Master’s degree in Mathematics
• Master’s degree in Artificial Intelligence
• Equivalent advanced degree in related quantitative fields
• Strong academic foundation in analytics and modeling
Certifications
• Machine Learning Engineer certification from major cloud providers
• Predictive analytics certification from recognized learning platforms
• MLOps and cloud AI certifications
• Exposure to AWS GCP or Azure based AI services
• Certification credentials considered beneficial but not mandatory
Additional Information
• Role operates within enterprise AI Labs environment
• Exposure to large scale real world datasets
• Opportunity to work on national infrastructure focused projects
• Learning driven culture with focus on applied AI outcomes
• Collaboration across multiple business verticals
• Strong emphasis on production ready AI systems
• Exposure to governance compliance and ethical AI practices
• Opportunity to build long term career in enterprise data science
• High impact role with measurable business outcomes
• Structured processes combined with innovation driven work culture
Please click here to apply.
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