Senior Data Scientist (Python/Coding/ML Deployment) (Gurugram)

- 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

- 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 recent 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 .