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.

• • • 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.
    • 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
    • Comfortable with AWS services for data/ML (e.g., S3, Glue, EMR/Spark, Lambda, SageMaker; Databricks), and integrating with enterprise data lakes/warehouses.
    • Proficient in Python and ML/DS libraries (Pandas, scikit ‑ learn, PyTorch/TensorFlow, XGBoost/LightGBM); strong software practices (testing, linting, packaging).
    • Strong SQL and data wrangling; experience with Spark/Databricks for large ‑ scale feature pipelines and training.
    • Working knowledge of data privacy, safe model behaviors, prompt filtering/output moderation, and auditability for regulated environments.
    • Exploratory data analysis and hypothesis testing to identify ML opportunities is a plus.
    • Experience with dashboards/BI (Power BI/Tableau) and experiment tracking (e.g., MLflow) is a plus.

• Educational Qualifications:
• Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related quantitative field.

• • Preferred Certifications:
    • AWS Certified Machine Learning - Specialty
    • AWS Certified Data Analytics - Specialty (or equivalent)
    • Databricks Machine Learning Professional and/or Databricks Generative AI Engineer (plus)
    • Certified Artificial Intelligence Practitioner (CAIP) or similar GenAI/Responsible AI certifications