Description

Key Responsibilities:

* Translate business problems into appropriate AI/ML problem statements, features, algorithms, and

evaluation frameworks

* Perform exploratory analysis and feature engineering on large enterprise datasets

* Design, train, tune, back-test, and validate machine-learning and statistical models

* Build models for use cases including price and cost prediction/benchmarking, demand forecasting,

supplier segmentation and consolidation, anomaly and leakage detection, clustering and pattern

identification, risk prediction, recommendation/next-best-action engines, and optimization and scenario

modelling

* Develop reusable model components and decision logic applicable across multiple business use cases

rather than creating one-off models

* Develop LLM/GenAI solutions for unstructured information such as contracts, specifications, supplier

documents, and market intelligence where appropriate

* Build RAG pipelines, embeddings, semantic search, and vector-based retrieval where relevant

* Evaluate model performance using appropriate statistical and business metrics and establish confidence

thresholds and explainability

* Work with business SMEs to validate whether model outputs make practical and commercial sense

* Package models for deployment through APIs/model-serving frameworks and work with developers to

integrate recommendations into user-facing applications

* Establish MLOps practices covering experiment tracking, model versioning, model registry, deployment,

monitoring, and retraining

* Monitor model accuracy, drift, and performance after deployment and establish feedback loops

incorporating actual business outcomes

Required Skills:

* Python

* Pandas

* NumPy

* scikit-learn

* Strong understanding of supervised and unsupervised machine learning

* Time-series modelling

* Clustering techniques

* Anomaly detection

* Statistical techniques

* Strong SQL skills

* Ability to work with large, imperfect enterprise datasets

* MLflow or model registries

* Git

* Docker

* APIs and CI/CD for productionizing ML models

* Experience deploying AI/ML workloads on AWS

* 4–5 years of hands-on experience in Data Science, Machine Learning, or AI Engineering

* Experience independently taking multiple models from business problem through data exploration,

modelling, validation, and deployment

* Ability to explain complex model outputs to non-technical business stakeholders

* Strong experimentation and problem-solving mindset

Preferred (Bonus) Skills:

* XGBoost

* LightGBM

* Optimization techniques

* Recommendation systems

* PyTorch

* TensorFlow

* Hands-on exposure to LLMs and prompt engineering

* Embeddings and vector databases

* RAG (Retrieval-Augmented Generation)

* Evaluation and guardrail approaches for GenAI

* LangChain

* LlamaIndex or equivalent GenAI frameworks

* AWS S3

* AWS EC2

* AWS SageMaker

* AWS Bedrock

* Experience with enterprise AI applications involving structured and unstructured data

Key Skills

Education

Bachelor's degree