Job Summary

Senior Data Scientist (Cognitive Engineering).

The Team: As a member of the EDO, Collection Platforms AI Cognitive Engineering team you will work on designing and building GenAI-driven and ML-powered products and capabilities to power natural language understanding, data extraction, information retrieval and data sourcing solutions for SP Global. You will help define AI strategy, mentor junior team members and drive production-ready AI products and pipelines while leading by example in a highly engaging work environment. You will work in a (truly) global team and be encouraged for thoughtful risk-taking and self-initiative.

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 up-to-date 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, QA 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

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