Build AI-powered systems using NLP, LLMs, and agentic workflows
Develop NLP solutions for tasks such as text classification, entity extraction, intent detection, summarization, and sentiment analysis
Design and implement agentic AI systems that reason over conversations, historical data, and business rules
Apply traditional machine learning techniques for prediction, classification, and optimization
Use operations research techniques (optimization, heuristics, simulations) to support logistics and operational decision-making
Collaborate with product, engineering, operations, and commercial teams to translate business problems into data science solutions
Build end-to-end pipelines: data ingestion, feature engineering, modeling, evaluation, deployment, and monitoring
Deploy models and AI services to production using cloud platforms and modern MLOps practices

1–3 years of experience in Data Science, Machine Learning, or applied AI roles
Strong foundation in data science and statistics
Solid experience with traditional machine learning (e.g., regression, tree-based models, clustering, time series)
Strong Python skills and familiarity with common ML/NLP libraries (scikit-learn, spaCy, NLTK, Hugging Face, etc.)
Understanding of software engineering fundamentals (APIs, version control, testing)
Hands-on knowledge of NLP:Text preprocessing and feature extractionClassical NLP models and modern embeddingsExperience with tasks like classification, NER, topic modeling, or text similarityExperience working with LLMs for NLP tasks (prompting, fine-tuning or adaptation, chaining, tool usage)

Ownership, autonomy, and collaboration in a fast-growing tech environment
Grow your skills in AI and Data Science.
Build high-impact AI, ML and Decision Support systems used daily by operations and commercial teams
Work on real production use cases, not just experiments

Associate Data Scientist

Key skills