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MLA 006 Salaries for Data Science & Machine Learning

Machine Learning Guide

Release Date: 07/19/2018

MLG 035 Large Language Models 2 show art MLG 035 Large Language Models 2

Machine Learning Guide

At inference, large language models use in-context learning with zero-, one-, or few-shot examples to perform new tasks without weight updates, and can be grounded with Retrieval Augmented Generation (RAG) by embedding documents into vector databases for real-time factual lookup using cosine similarity. LLM agents autonomously plan, act, and use external tools via orchestrated loops with persistent memory, while recent benchmarks like GPQA (STEM reasoning), SWE Bench (agentic coding), and MMMU (multimodal college-level tasks) test performance alongside prompt engineering techniques such as...

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Machine Learning Guide

Explains language models (LLMs) advancements. Scaling laws - the relationships among model size, data size, and compute - and how emergent abilities such as in-context learning, multi-step reasoning, and instruction following arise once certain scaling thresholds are crossed. The evolution of the transformer architecture with Mixture of Experts (MoE), describes the three-phase training process culminating in Reinforcement Learning from Human Feedback (RLHF) for model alignment, and explores advanced reasoning techniques such as chain-of-thought prompting which significantly improve complex...

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Machine Learning Guide

Tool use in code AI agents allows for both in-editor code completion and agent-driven file and command actions, while the Model Context Protocol (MCP) standardizes how these agents communicate with external and internal tools. MCP integration broadens the automation capabilities for developers and machine learning engineers by enabling access to a wide variety of local and cloud-based tools directly within their coding environments. Links Notes and resources at  stay healthy & sharp while you learn & code Tool Use in Code AI Agents Code AI agents offer two primary modes of...

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Machine Learning Guide

Gemini 2.5 Pro currently leads in both accuracy and cost-effectiveness among code-focused large language models, with Claude 3.7 and a DeepSeek R1/Claude 3.5 combination also performing well in specific modes. Using local open source models via tools like Ollama offers enhanced privacy but trades off model performance, and advanced workflows like custom modes and fine-tuning can further optimize development processes. Links Notes and resources at  stay healthy & sharp while you learn & code Model Current Leaders According to the  (as of April 12, 2025), leading...

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Machine Learning Guide

Vibe coding is using large language models within IDEs or plugins to generate, edit, and review code, and has recently become a prominent and evolving technique in software and machine learning engineering. The episode outlines a comparison of current code AI tools - such as Cursor, Copilot, Windsurf, Cline, Roo Code, and Aider - explaining their architectures, capabilities, agentic features, pricing, and practical recommendations for integrating them into development workflows. Links Notes and resources at   stay healthy & sharp while you learn & code Definition and...

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Machine Learning Guide

Links: Notes and resources at 3Blue1Brown videos:   stay healthy & sharp while you learn & code  audio/video editing with AI power-tools Background & Motivation RNN Limitations: Sequential processing prevents full parallelization—even with attention tweaks—making them inefficient on modern hardware. Breakthrough: “Attention Is All You Need” replaced recurrence with self-attention, unlocking massive parallelism and scalability. Core Architecture Layer Stack: Consists of alternating self-attention and feed-forward (MLP) layers, each wrapped...

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Machine Learning Guide

Databricks is a cloud-based platform for data analytics and machine learning operations, integrating features such as a hosted Spark cluster, Python notebook execution, Delta Lake for data management, and seamless IDE connectivity. Raybeam utilizes Databricks and other ML Ops tools according to client infrastructure, scaling needs, and project goals, favoring Databricks for its balanced feature set, ease of use, and support for both startups and enterprises. Links Notes and resources at   stay healthy & sharp while you learn & code Raybeam and Databricks Raybeam is a...

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Machine Learning Guide

Machine learning pipeline orchestration tools, such as SageMaker and Kubeflow, streamline the end-to-end process of data ingestion, model training, deployment, and monitoring, with Kubeflow providing an open-source, cross-cloud platform built atop Kubernetes. Organizations typically choose between cloud-native managed services and open-source solutions based on required flexibility, scalability, integration with existing cloud environments, and vendor lock-in considerations. Links Notes and resources at   stay healthy & sharp while you learn & code  - Data Scientist...

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Machine Learning Guide

The deployment of machine learning models for real-world use involves a sequence of cloud services and architectural choices, where machine learning expertise must be complemented by DevOps and architecture skills, often requiring collaboration with professionals. Key concepts discussed include infrastructure as code, cloud container orchestration, and the distinction between DevOps and architecture, as well as practical advice for machine learning engineers wanting to deploy products securely and efficiently. Links Notes and resources at   stay healthy & sharp while you learn...

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Machine Learning Guide

AWS development environments for local and cloud deployment can differ significantly, leading to extra complexity and setup during cloud migration. By developing directly within AWS environments, using tools such as Lambda, Cloud9, SageMaker Studio, client VPN connections, or LocalStack, developers can streamline transitions to production and leverage AWS-managed services from the start. This episode outlines three primary strategies for treating AWS as your development environment, details the benefits and tradeoffs of each, and explains the role of infrastructure-as-code tools such as...

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O'Reilly's 2017 Data Science Salary Survey finds that location is the most significant salary determinant for data professionals, with median salaries ranging from $134,000 in California to under $30,000 in Eastern Europe, and highlights that negotiation skills can lead to salary differences as high as $45,000. Other key factors impacting earnings include company age and size, job title, industry, and education, while popular tools and languages—such as Python, SQL, and Spark—do not strongly influence salary despite widespread use.

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Global and Regional Salary Differences

  • Median Global Salary: $90,000 USD, up from $85,000 the previous year.
  • Regional Breakdown:
    • United States: $112,000 median; California leads at $134,000.
    • Western Europe: $57,000—about half the US median.
    • Australia & New Zealand: Second after the US.
    • Eastern Europe: Below $30,000.
    • Asia: Wide interquartile salary range, indicating high variability.

Demographic and Personal Factors

  • Gender: Women's median salaries are $8,000 lower than men's. Women make up 20% of respondents but are increasing in number.
  • Age & Experience: Higher age/experience correlates with higher salaries, but the proportion of older professionals declines.
  • Education: Nearly all respondents have at least a master's; PhD holders earn only about $5,000 more than those with a master’s.
  • Negotiation Skills: Self-reported strong salary negotiation skills are linked to $45,000 higher median salaries (from $70,000 for lowest to $115,000 for highest bargaining skill).

Industry, Company, and Role

  • Industry Impact:
    • Highest salaries found in search/social networking and media/entertainment.
    • Education and non-profit offer the lowest pay.
  • Company Age & Size:
    • Companies aged 2–5 years offer higher than average pay; less than 2 years old offer much lower salaries (~$40,000).
    • Large organizations generally pay more.
  • Job Title:
    • "Data scientist" and "data analyst" titles carry higher medians than "engineer" titles by around $7,000.
    • Executive titles (CTO, VP, Director) see the highest pay, with CTOs at $150,000 median.

Tools, Languages, and Technologies

  • Operating Systems:
    • Windows: 67% usage, but declining.
    • Linux: 55%; Unix: 18%; macOS: 46%; Unix-based systems are rising in use.
  • Programming Languages:
    • SQL: 64% (most used for database querying).
    • Python: 63% (most popular procedural language).
    • R: 54%.
    • Others (Java, Scala, C/C++, C#): Each less than 20%.
    • Salary difference across languages is minor; C/C++ users earn more but not enough to outweigh the difficulty.
  • Databases:
    • MySQL (37%), MS SQL Server (30%), PostgreSQL (28%).
    • Popularity of the database has little impact on pay.
  • Big Data and Search Tools:
    • Spark: Most popular big data platform, especially for large-scale data processing.
    • Elasticsearch: Most common search engine, but Solr pays more.
  • Machine Learning Libraries:
    • Scikit-learn (37%) and Spark MLlib (16%) are most used.
  • Visualization Tools:
    • R’s ggplot2 and Python’s matplotlib are leading choices.

Key Salary Differentiators (per Machine Learning Analysis)

  • Top Predictors (explaining ~60% of salary variance):
    • World/US region
    • Experience
    • Gender
    • Company size
    • Education (but amounting to only ~$5,000 difference)
    • Job title
    • Industry
  • Lesser Impact: Specific tools, languages, and databases do not meaningfully affect salary.

Summary Takeaways

  • The greatest leverage for a higher salary comes from geography and individual negotiation capability, with up to $45,000 differences possible.
  • Role/title selection, industry, company age, and size are also significant, while mastering the most commonly used tools is essential but does not strongly differentiate pay.
  • For aspiring data professionals: focus on developing negotiation skills and, where possible, optimize for location and title to maximize earning potential.