Full-Time
Data Scientist 3
S&P Global
10,001+ employees
Delivers credit ratings, market data, indices
No salary listed
Hyderabad, Telangana, India
Hybrid
Bachelor's
 Data & Analytics (1) |
|---|
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- A university degree in Computer Science, Engineering, Mathematics, or a related discipline, or equivalent practical experience.
- At least 3 years of hands-on programming experience, primarily in Python, focused on building, training, and evaluating machine learning and deep learning models.
- Strong experience with machine learning frameworks and libraries such as PyTorch, TensorFlow, Keras, and scikit-learn, including designing and implementing neural network architectures.
- Proven experience with computer vision techniques and applications, including image and video processing, feature extraction, object detection, segmentation, and representation learning.
- Experience developing end-to-end machine learning workflows from data preparation and feature engineering through model training, validation, and deployment, with emphasis on model performance, robustness, and maintainability.
- Working knowledge of data storage and querying technologies, including SQL and/or NoSQL, to support analytical and machine learning workflows.
- Hands-on experience using cloud platforms such as AWS, Azure, or Google Cloud Platform for machine learning experimentation, training at scale, and model deployment.
- Familiarity with distributed and accelerated computing concepts relevant to machine learning workloads, such as multi-GPU training, distributed training, or large-scale inference.
- Experience collaborating with data engineers and platform teams to integrate models into production pipelines while maintaining ownership of model logic and performance.
- Familiarity with version control systems such as Git and best practices for collaborative machine learning development.
- Ability to communicate machine learning results, trade-offs, and limitations clearly to technical and non-technical stakeholders.
- Collaborate with stakeholders, including data scientists, analysts, engineers, and business teams, to understand data needs and translate them into actionable technical requirements.
- Design, build, and maintain scalable data pipelines using modern tools and cloud platforms such as AWS, Azure, or Google Cloud Platform.
- Develop and maintain data infrastructure for analytics and machine learning using cloud-native technologies and services.
- Improve data quality, lineage, and reliability, and troubleshoot issues across the data lifecycle.
- Support the deployment and monitoring of analytics and machine learning solutions in production while working closely with data science and engineering peers.
- Contribute to an Agile team environment through documentation, code reviews, and iterative delivery.
- Exposure to workflow orchestration or experiment management tools such as Airflow, MLflow, or similar tools for reproducible machine learning pipelines.
- Experience with data governance practices, including privacy, security, access controls, and compliance expectations.
- Experience improving data observability through monitoring, alerting, service-level agreements, and incident response.
S&P Global supplies financial information, analytics, and benchmarks to investors, corporations, and governments. Its offerings include credit ratings, market intelligence, and indices, along with price assessments and energy data. Clients access these tools through subscriptions, licensing, and transaction-based services, integrating data and research into their workflows. The company aims to help clients assess risk, make informed decisions, and drive growth while upholding corporate responsibility and ESG commitments.
Company Size
10,001+
Company Stage
IPO
Headquarters
New York City, New York
Founded
1917
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Simplify's Take
What believers are saying
- Q2 2026 pro forma revenue rose 11%, with adjusted EPS up 23%.
- Ratings grew 17% and Indices grew 20% in Q2 2026.
- Management reiterated 2026 guidance and planned over $7 billion of buybacks.
What critics are saying
- Saugata Saha left Market Intelligence in July 2026, creating execution risk during integration.
- Market Intelligence and Energy grew only 6% and 2% in Q2 2026.
- If Microsoft commoditizes discovery, S&P Global’s premium data moat erodes fast.
What makes S&P Global unique
- July 2026 Mobility spin-off sharpened S&P Global into higher-margin data, ratings, and indices.
- S&P Global Ratings remains a trusted gatekeeper for debt markets and issuer access.
- Microsoft 365 Copilot integration embeds proprietary data directly inside enterprise analyst workflows.
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Benefits
Health Insurance
Unlimited Paid Time Off
Professional Development Budget
401(k) Company Match
Family Planning Benefits
Employee Discounts
Company News
The S&P 500's dividend yield has fallen to a record low of just above 1%, according to Charlie Bilello, chief market strategist at Creative Planning. Whilst dividend payouts haven't decreased, stock prices have risen much faster, particularly amongst megacap technology companies that pay little or nothing in dividends. The shift is forcing retirees to adapt their strategies. Steven Yedlin, a 75-year-old retired doctor, has stopped automatically reinvesting dividends and now directs them to high-yield money-market funds instead. Recent dividend suspensions at Papa John's and UWM Holdings highlight the risks. Papa John's scrapped its quarterly payout following an 8.8% revenue decline to $482.4 million, choosing to redirect funds toward franchise incentives and technology improvements instead.
Wolfe Research reports strong second-quarter earnings momentum for S&P 500 companies, with 69% of the 465 firms that had reported by Wednesday beating revenue forecasts. The dollar-weighted revenue surprise reached 3.8%. Corporate guidance for the third quarter shows unusual confidence, with 64% of the 86 companies providing guidance offering midpoints above consensus—the highest proportion since the COVID period. The firm expects S&P 500 operating earnings per share to grow 31% in 2026, or approximately 27% when adjusted for one-time gains from mega-cap technology companies. Wolfe characterises this as the strongest fundamental environment outside a post-recession recovery in over 50 years. Sustainability of growth into 2027 remains uncertain, particularly given heavy capital expenditure on artificial intelligence.
Expand Energy stands out among three companies popular with Wall Street analysts, according to StockStory's independent analysis. The natural gas and oil producer, formerly Chesapeake Energy, achieved 19.4% annual revenue growth over five years. Its $12.66 billion revenue base provides strong negotiating leverage with suppliers. The company also improved its EBITDA profits and efficiency during this period. In contrast, analysts may be overlooking risks at S&P Global and MSCI, despite bullish consensus price targets suggesting upside of 23.9% and 22.3% respectively. S&P Global's earnings per share growth of 8.5% annually lagged behind revenue gains over the past five years. MSCI shows negative return on equity, indicating management lost money attempting to expand the business. The analysis notes that analysts rarely issue sell ratings, partly because their firms often seek business from covered companies.
Bill Ackman's Pershing Square disclosed stakes of approximately $1.1 billion each in S&P Global, Visa, and Mastercard in its second-quarter 13F filing. All three companies collect fees on activity flowing through their systems without taking on lending risk. S&P Global closed at $418.80, about 28% below its 52-week high of $579.05. Visa and Mastercard traded near their highs, roughly 3% and 5% below respectively. S&P Global's gap partly reflects its July spin-off of Mobility division, which trades around $20 per share. The company reported second-quarter revenue of $3.7 billion, up 11% year over year, excluding Mobility. Ratings revenue climbed 17% to $1.3 billion, whilst Indices grew 20% to $534 million. However, Market Intelligence and Energy divisions grew just 6% and 2% respectively. Non-GAAP earnings per share rose 23% to $4.83.
S&P Global Inc. has raised $300 million through senior unsecured fixed-rate notes under its global medium-term note programme. The notes, rated BBB by S&P Global Ratings and Baa3 by Moody's, will be listed on the Global Securities Market and Debt Securities Market of the India International Exchange (NSE IFSC). The issuance forms part of a broader funding strategy to support expansion plans, including investments in AI-driven analytics and data-platform development. By tapping Indian debt markets, S&P Global benefits from preferential tax rates offered through the IFSC framework whilst diversifying its capital-raising channels beyond traditional US debt markets. The transaction demonstrates how multinational firms are increasingly exploring emerging-market debt listings to access new investor pools and optimise funding costs.