Data Scientist - Lead

What will you do?


* Collaborate with the AI Product Owner to understand the business requirements and define appropriate modelling approaches, experimentation plans, and success metrics.


* Coordinate with business teams to monitor model outcomes, gather feedback, and refine/improve machine learning models based on performance insights.


* Lead data discovery, feature engineering, experimentation, offline/online evaluation, and productionization with CI/CD for ML; own model documentation, reproducibility, and traceability.


* Apply supervised/unsupervised/deep learning, NLP, and LLM techniques (including RAG pipelines, prompt engineering, vector search, and safety guardrails) where they create clear value.


* Design and execute rigorous evaluation strategies for ML and GenAI models, including offline metrics, human-in-the-loop reviews for GenAI outputs, regression checks, and failure mode analysis.


* Implement governance frameworks for AI models - applying bias/fairness checks, safety filters, responsible AI controls, and executing evaluation protocols defined by Business.


* Collaborate with data/ML engineers to industrialize models via APIs/batch jobs, feature stores, scalable serving, and monitoring for drift, performance, cost, and latency.


* Lead data mining, collection, and quality initiatives across structured, semi-structured, and unstructured data to ensure integrity, lineage, and compliance.


* Maintain rigorous experiment tracking using tools, ensuring reproducibility and clear lineage across model iterations and experiments.


* Adhere to stringent quality assurance and documentation standards using version control and code repositories (e.g., Git, GitHub, Markdown)


* Mentor and lead data scientists, conduct design/code reviews, and cultivate best practices in experimentation, evaluation, and documentation.


* Track emerging tools/techniques in ML/GenAI and drive reusable frameworks, templates, and SDK/API-based accelerators to industrialize solutions across the organization.

What skills and capabilities will make you successful?

Technical Experience:


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o 5-8 years of hands-on experience across classical ML (tree-based methods, GLMs), deep learning (PyTorch/TensorFlow), and NLP/LLMs (tokenization, embeddings, fine-tuning, instruction-tuning, RAG).

Hands-on with evaluation and safety/guardrail patterns for production GenAI.
o Familiarity with ML lifecycle platforms (such as SageMaker, Azure ML, or Databricks) to run experiments, track models, and provide well-structured model artifacts to ML Engineers for deployment
o Comfortable with AWS services for data/ML (e.g., S3, Glue, EMR/Spark, Lambda, SageMaker; Databricks), and integrating with enterprise data lakes/warehouses.
o Proficient in Python and ML/DS libraries (Pandas, scikit-learn, PyTorch/TensorFlow, XGBoost/LightGBM); strong software practices (tes...