In this role as an AI/ML Scientist on the Global Data and Analytics (GDA) team, you will support the development of strategic, visibility-driven recommendation systems that serve both internal stakeholders and external customers. This initiative aims to deliver actionable insights that enhance supply chain execution, support strategic decision-making, and enable innovative service offerings.
You should be able to design, develop, and implement machine learning models, conduct deep data analysis, and support decision-making with data-driven insights. Responsibilities include building and validating predictive models, supporting experiment design, and integrating advanced techniques like transformers, GANs, and reinforcement learning into scalable production systems. The role requires solving complex problems using NLP, deep learning, optimization, and computer vision. You should be comfortable working independently, writing reliable code with automated tests, and contributing to debugging and refinement. You ll also document your methods and results clearly and collaborate with cross-functional teams to deliver high-impact AI/ML solutions that align with business objectives and user needs. What Ill be doing your accountabilities?
- Design, develop, and implement machine learning models, conduct in-depth data analysis, and support decision-making with data-driven insights
- Develop predictive models and validate their effectiveness
- Support the design of experiments to validate and compare multiple machine learning approaches
- Research and implement cutting-edge techniques (e.g., transformers, GANs, reinforcement learning) and integrate models into production systems, ensuring scalability and reliability
- Apply creative problem-solving techniques to design innovative models, develop algorithms, or optimize workflows for data-driven tasks
- Independently apply data-driven solutions to ambiguous problems, leveraging tools like Natural Language Processing, deep learning frameworks, machine learning, optimization methods and computer vision frameworks
- Understand technical tools and frameworks used by the team, including programming languages, libraries, and platforms and actively support debugging or refining code in projects
- Write and integrate automated tests alongside their models or code to ensure reproducibility, scalability, and alignment with established quality standards
- Contribute to the design and documentation of AI/ML solutions, clearly detailing methodologies, assumptions, and findings for future reference and cross-team collaboration
- Collaborate across teams to develop and implement high-quality, scalable AI/ML solutions that align with business goals, address user needs, and improve performance
Foundational Skills
- Mastered Data Analysis and Data Science concepts and can demonstrate this skill in complex scenarios
- AI & Machine Learning, Programming and Statistical Analysis Skills beyond the fundamentals and can demonstrate the skills in most situations without guidance.
Specialized Skills
- To be able to understand beyond the fundamentals and can demonstrate in most situations without guidance:
- Data Validation and Testing
- Model Deployment
- Machine Learning Pipelines
- Deep Learning
- Natural Language Processing (NPL)
- Optimization & Scientific Computing
- Decision Modelling and Risk Analysis.
- To understand fundamentals and can demonstrate this skill in common scenarios with guidance:
Qualifications & Requirements
- Bachelor s degree in B.E./B.Tech , preferably in Computer Science , Data Science , Mathematics , Statistics , or related fields.
- Strong practical understanding of:
- Machine Learning algorithms (classification, regression, clustering, time-series)
- Statistical inference and probabilistic modeling
- Data wrangling , feature engineering, and preprocessing at scale
- Proficiency in collaborative development tools:
- IDEs (e.g., VS Code, Jupyter), Git/GitHub , CI/CD workflows , unit and integration testing
- Excellent coding and debugging skills in Python (preferred), with knowledge of SQL for large-scale data operations
- Experience working with:
- Versioned data pipelines , model reproducibility , and automated model testing
- Ability to work in agile product teams, handle ambiguity, and communicate effectively with both technical and business stakeholders
- Passion for continuous learning and applying AI/ML in impactful ways
Preferred Experiences
- 8+ years of experience in AI/ML or Data Science roles, working on applied machine learning problems in production settings
- 7+ years of hands-on experience with:
- Apache Spark , distributed computing, and large-scale data processing
- Deep learning using TensorFlow or PyTorch
- Model serving via REST APIs , batch/streaming pipelines , or ML platforms
- Hands-on experience with:
- Cloud-native development (Azure preferred; AWS or GCP also acceptable)
- Databricks , Azure ML , or SageMaker platforms
- Experience with Docker , Kubernetes , and orchestration of ML systems in production
- Familiarity with A/B testing , causal inference , business impact modeling
- Exposure to visualization and monitoring tools: Power BI , Superset , Grafana
- Prior work in logistics , supply chain , operations research , or industrial AI use cases is a strong plus
