MLA 006 Salaries for Data Science & Machine Learning
Release Date: 07/19/2018
MLA 030 AI and Programming Jobs: What Happened and How to Position
Machine Learning Guide
The aggregate job market held, the entry-level door narrowed, and software postings sit a quarter below pre-pandemic. Why cheap implementation made specification, verification and domain scarce, how ML roles split five ways, and how to position. Links - stay healthy & sharp while you learn & code - this one has siblings, each on its own subject and produced the same way What coding agents did to programming and machine learning jobs by late 2026: the labor data, the mechanism behind it, how the ML career splintered into five roles, and a concrete positioning plan. Companion...
info_outline
MLA 029 OpenClaw and Personal Agents
Machine Learning Guide
OpenClaw as the worked example of the always-on personal agent: gateway, markdown memory, heartbeats, skills, coding agents from your phone, hosted vs local models, and the 2026 security record (exposed instances, two critical CVEs, ClawHavoc) with the posture that makes it survivable. Links - stay healthy & sharp while you learn & code - this one has siblings, each on its own subject and produced the same way Second and last episode of the agents pair. covered the theory: loops, tools, memory, protocols, SDKs, evaluation. This one takes a single category, the always-on...
info_outline
MLA 028 AI Agents: Loops, Tools, Memory, Protocols, and Evaluation
Machine Learning Guide
What an AI agent actually is, why coding agents got good first, how memory really works, what MCP and A2A standardize, which SDKs are alive, how to evaluate on trajectories, and where the products stand after browser agents contracted. Links - stay healthy & sharp while you learn & code - this one has siblings, each on its own subject and produced the same way First of two episodes on AI agents. This one is the architecture: the loop, tools and verifiable feedback, memory, the protocols (MCP, A2A, computer use), the SDK landscape, evaluation and observability, the product...
info_outline
MLA 027 The AI Media Pipeline: Voice, Music, ComfyUI, APIs, and Finishing
Machine Learning Guide
How to automate AI media end to end: clone your own voice on open TTS, pick music that's actually licensed, run ComfyUI graphs headless, design around fal, Replicate and provider queues, finish with ffmpeg, and stay inside licensing at every layer. Links - this one has siblings, each on its own subject and produced the same way Companion show. This episode is the overview of the media pipeline. For weekly, hands-on coverage of the video half, from a first usable clip to scenes that cut together, listen to on Gnothi. - stay healthy & sharp while you learn & code Pipeline,...
info_outline
MLA 026 AI Video Generation 2026: Veo, Gemini, Kling, Runway, MiniMax, Sora
Machine Learning Guide
Sora is shut down, Google runs two video models, Kling 3 does lip-synced dialogue, and open-weight MiniMax H3 is what you can actually fine-tune. What a usable clip costs, which models do native audio, how reference consistency works, and why the unit of work is the shot. Links Notes and resources at - this one has siblings, each on its own subject and produced the same way. Companion show: for weekly, hands-on coverage of the AI video pipeline, from a first usable clip to scenes that cut together, listen to . - stay healthy & sharp while you learn & code Second...
info_outline
MLA 025 AI Image Generation 2026: GPT Image, Nano Banana, Midjourney, Flux
Machine Learning Guide
Editing replaced generation as the core task. How GPT Image 2.5, Google's Nano Banana line, Midjourney V8.2 and Flux 2 differ, what open weights and LoRAs buy you, ControlNet vs instruction editing, and how licensing and C2PA provenance work now. Links - this one has siblings, each on its own subject and produced the same way Companion show. This episode is the overview. For weekly, hands-on coverage of the full image and video pipeline, from a first usable clip to scenes that cut together, listen to . - stay healthy & sharp while you learn & code First of three episodes on...
info_outline
MLG 036 Autoencoders
Machine Learning Guide
Auto encoders are neural networks that compress data into a smaller "code," enabling dimensionality reduction, data cleaning, and lossy compression by reconstructing original inputs from this code. Advanced auto encoder types, such as denoising, sparse, and variational auto encoders, extend these concepts for applications in generative modeling, interpretability, and synthetic data generation. Links Notes and resources at - stay healthy & sharp while you learn & code Build the future of multi-agent software with . Thanks to from for recording...
info_outline
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...
info_outline
MLG 034 Large Language Models 1
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...
info_outline
MLA 024 Agentic Software Engineering: Specs, Verification, and the Review Loop
Machine Learning Guide
How working engineers ship with coding agents: issues an agent can verify, plan mode before code, a verification loop with a browser in it, agent review of agent code, worktrees and CI, cost discipline, and where agents still fail. Links - stay healthy & sharp while you learn & code - this one has siblings, each on its own subject and produced the same way Third and last episode of the vibe-coding sequence. picked an agent; explained the mechanics. This one is the practice: how working engineers ship real software with Claude Code, Codex, and Antigravity without...
info_outlineO'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.
Links
- Notes and resources at ocdevel.com/mlg/mla-6
- Try a walking desk stay healthy & sharp while you learn & code
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.