Data Scientist

Job Description

About the Role: Grade Level (for internal use): 09 The Team: As a member of the Data Transformation 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 our clients. You will spearhead development of 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. The Impact: The Data Transformation team has already delivered breakthrough products and significant business value over the last 3 years. In this role you will be developing our next generation of new products while enhancing existing ones aiming at solving high-impact business problems. Whats in it for you: Be a part of a global company and build solutions at enterprise scale Collaborate with a highly skilled and technically strong team Contribute to solving high complexity, high impact problems Key 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 Performing exploratory data analysis, proof-of-concept modelling, model benchmarking and setup model validation experiments Training large models both for experimentation and production Develop production ready pipelines for enterprise scale projects Perform code reviews & optimization for your projects and team Spearhead deployment and model scaling strategies Stakeholder management and representing the team in front of our leadership Leading and mentoring by example including project scrums What Were Looking For: 2+ years of professional experience in Data Science domain Expertise in Python (Numpy, Pandas, Spacy, Sklearn, Pytorch/TF2, HuggingFace etc.) Experience with SOTA models related to NLP and expertise in text matching techniques, including sentence transformers, word embeddings, and similarity measures Expertise in probabilistic machine learning model for classification, regression & clustering Strong experience in feature engineering, data preprocessing, and building machine learning models for large datasets. Exposure to Information Retrieval, Web scraping and Data Extraction at scale OOP Design patterns, Test-Driven Development and Enterprise System design SQL (any variant, bonus if this is a big data variant) Linux OS (e.g. bash toolset and other utilities) Version control system experience with Git, GitHub, or Azure DevOps. Problem-solving and debugging skills Software craftsmanship, adherence to Agile principles and taking pride in writing good code Techniques to communicate change to non-technical people Nice to have Prior work to show on Github, Kaggle, StackOverflow etc. Cloud expertise (AWS and GCP preferably) Expertise in deploying machine learning models in cloud environments Familiarity in working with LLMs