Data Science Job Market Trends

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Model Context Protocol (MCP)

500-1K
Employers
+1648%
Growth

An open standard that standardizes how AI models integrate and share data with external tools, systems, and data sources. It provides a universal interface for reading files, executing functions, and handling contextual prompts.

LlamaIndex

900-1.8K
Employers
+474%
Growth

A suite of tools and models for working with LLMs, offering capabilities for fine-tuning, querying, and managing large language models.

AI Ethics

500-1K
Employers
+392%
Growth

The field of study that deals with the moral implications and responsibilities of artificial intelligence. It focuses on ensuring AI systems are designed and used in ways that adhere to ethical standards and benefit society.

FastAPI

4K-8K
Employers
+354%
Growth

FastAPI is a modern, high-performance web framework for building APIs with Python. It is designed to be easy to use and fast, leveraging Python's type hints to provide automatic validation and documentation of API endpoints.

Scale AI

500-1K
Employers
+343%
Growth

A company that provides data annotation services for machine learning models, specializing in image, text, and video datasets. Their platform enables organizations to efficiently and accurately label data for AI model training.

Vertex AI

1K-2K
Employers
+301%
Growth

A comprehensive platform by Google Cloud for developing, training, and deploying machine learning models.

Artificial Intelligence Engineer

2K-4K
Employers
+282%
Growth

Develops and implements AI models and algorithms to solve complex problems and improve decision-making processes. Essential for advancing automation and technology-driven solutions in various industries.

Data Lakehouse

700-1.4K
Employers
+253%
Growth

A data architecture combining features of data lakes and data warehouses to support both analytical and transactional workloads. Allows for efficient multi-modal data processing and storage with unified governance.

MLflow

2K-4K
Employers
+238%
Growth

An open-source platform for managing the end-to-end machine learning lifecycle, including experimentation, reproducibility, and deployment.