S&P Global is seeking a Data Analyst to join our team in Gurgaon, Ahmedabad, Hyderabad, Noida. This role involves generating unique, market-leading data by ensuring accuracy, completeness, and timeliness. Ideal candidates will have a strong analytical mindset and a background in finance, economics, data science, or related fields. Join S&P Global to contribute to impactful insights that drive the financial industry.
Interested candidates can apply online as soon as possible. The detailed eligibility and application process are given below.
S&P Global is Hiring for Data Analyst Role in Multiple Locations – Overview
| Company Name: | S&P Global |
| Job Role: | Data Analyst |
| Location: | Gurgaon, Ahmedabad, Hyderabad, Noida |
| Work Mode: | On-Site |
| Last Date to Apply: | ASAP |
Skills:
- 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)
Responsibility:
- 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
