Senior Data Scientist (Python/Coding/ML Deployment)

S&P Global Market Intelligence; Gurugram District; Hybrid; INR 4,200,000 - 6,600,000; Full time

Job summary

S&P Global Market Intelligence is seeking a data science leader to define AI roadmaps, govern model building, and deploy GenAI-powered products in a production environment. You will lead end-to-end projects, mentor junior scientists, and partner with business stakeholders in a fast-paced setting.

The role emphasizes building scalable ML/GenAI solutions, implementing robust MLOps practices, and delivering measurable value in financial market intelligence contexts.

Qualifications

• Experience with large language models (LLMs) and prompt engineering; fine-tuning and embedding-based retrieval.
• Ability to architect, develop and deploy ML/GenAI-powered products and pipelines.
• Design and implement ML Ops practices including CI/CD, experiment tracking, and model versioning.
• Own end-to-end data science projects from requirements to deployment and monitoring.
• Experience in Economics/Financial market-intelligence domains is a plus.

Responsibilities

• Define AI roadmap, tooling choices, and best practices for model building and retrieval systems.
• Architect, develop and deploy large-scale ML/GenAI-powered products and pipelines.
• Lead MLOps: CI/CD, experiment tracking, model versioning, automated testing.
• Manage data science lifecycle: requirements, impact analysis, stakeholder alignment, and communication.
• Drive deployment, monitoring, and scaling of models in production; mentor junior data scientists.
• Collaborate with business leaders to ensure timely delivery and measurable outcomes.

Skills

Leadership experience

Stakeholder management

Research synthesis

Education

Masters or Bachelors in CS/Stats/Math

Tools

Python stack (NumPy, Pandas, SpaCy, scikit-learn, PyTorch/TF2, Transformers)

MLOps & deployment tools (Redis, Flask/Django/FastAPI, MLflow)

NLP/ML architectures (Transformers, GNNs, Multimodal)

Job description

Responsibilities:

• Define AI roadmap, tooling choices, and best practices for model building, prompt engineering, fine-tuning, and vector retrieval systems
• Architect, develop and deploy large-scale ML and GenAI-powered products and pipelines
• Design and implement MLOps practices including CI/CD pipelines for ML models, experiment tracking, model versioning, and automated testing frameworks.
• Own all stages of the data science project lifecycle, including:
• Identification and scoping of high-value data science and AI opportunities
• Partnering with business leaders, domain experts, and end-users to gather requirements and align on success metrics
• Evaluation, interpretation, and communication of results to executive stakeholders
• Lead exploratory data analysis, proof-of-concepts, model benchmarking, and validation experiments for both ML and GenAI approaches
• Establish and enforce coding standards, perform code reviews, and optimize data science workflows
• Drive deployment, monitoring, and scaling strategies for models in production (including both ML and GenAI services)
• Mentor and guide junior data scientists; foster a culture of continuous learning and innovation
• Manage stakeholders across functions to ensure alignment and timely delivery

Technical Requirements:

• Hands-on experience with large language models (e.g., Gemini, OpenAI, Anthropic, Llama), prompt engineering, fine-tuning/customization, and embedding-based retrieval
• Expert proficiency in Python (NumPy, Pandas, SpaCy, scikit-learn, PyTorch/TF 2, Hugging Face Transformers)
• Deep understanding of ML & Deep Learning models, including architectures for NLP (e.g., transformers), GNNs, and multimodal systems
• Strong grasp of statistics, probability, and the mathematics underpinning modern AI
• Ability to surf and synthesize current AI/ML research, with a track record of applying new methods in production
• Proven experience on at least one end-to-end GenAI or advanced NLP project: custom NER, table extraction via LLMs, Q&A systems, summarization pipelines, OCR integrations, or GNN solutions
• Familiarity with MLOps and deployment tools: Redis, Flask/Django/FastAPI, SQL, R-Shiny/Dash/Streamlit, MLflow
• Openness to evaluate and adopt emerging technologies and programming languages as needed

Good to have:

• Masters or Bachelors in Computer Science, Statistics, Mathematics, or related field
• 4-8 years of relevant experience in Data Science/AI, with at least 2 years in a leadership or technical lead role
• Prior experience in the Economics/Financial industry, especially with market-intelligence or risk analytics products
• Public contributions or demos on GitHub, Kaggle, StackOverflow, technical blogs, or publications