The Team
As a member of the Data Transformation - Cognitive Engineering team you will work on building ML powered products and capabilities to power natural language understanding, data extraction, information retrieval and data sourcing solutions for S&P Global Market Intelligence and other clients. You will learn how to develop 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 encouraged for thoughtful risk-taking and self-initiative.
What’s In It For You
- Be a part of a global company and build solutions at enterprise scale
- Work with a highly skilled and hands-on technical team (including leadership)
- Contribute to solving high complexity, high impact problems
- Build production ready pipelines from ideation to deployment
Responsibilities
- Design, Develop and Deploy ML powered products and pipelines
- Play a central role in all stages of the data science project life cycle, including:
- Identification of suitable data science project opportunities
- Partnering with business leaders, domain experts, and end-users to gain business understanding, data understanding, and collect requirements
- Evaluation/interpretation of results and presentation to business leaders
- Perform exploratory data analysis, proof-of-concept modelling, model benchmarking and setup model validation experiments
- Develop production ready pipelines for enterprise scale projects
- Perform code reviews & optimization
- Participate in deployment and model scaling strategies
- Stakeholder management
Technical Requirements
- Expert proficiency in Python (Numpy, Pandas, Spacy, Sklearn, Pytorch/TF2, HuggingFace etc.)
- Good knowledge & learning aptitude towards ML & Deep Learning models
- Good understanding of statistics and mathematics behind machine learning
- Ability to read current research in the AI space
- Experience with at least 1 project related to Table Extraction, NLP based Q&A, Summarization, Phrase extraction, custom NER models, OCR or GNNs.
- Exposure some of the following technologies - R-Shiny/Dash/Streamlit, SQL, Docker, Airflow, Redis, Flask/Django/FastAPI, PySpark
- Open to learning new technologies and programming languages as required
Good To Have
- A Master’s degree (minimum Bachelor’s degree) from a recognized institute
- Around 2-3 years of relevant experience in Data Science domain
- Prior experience from the Economics/Financial industry
- Some prior work to show on Github, Kaggle, StackOverflow etc.