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
- Solid 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

📌 Senior Data Scientist (Python/Coding/ML Deployment) (Haryana)
🏢 S&P Global Market Intelligence
📍 Haryana