Senior Data Scientist
Raidriar7170/hermes-skilleval
World-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics.
A skill your agent uses when the user asks to "design an experiment", "build a predictive model", "run A/B test analysis", "perform causal inference", "engineer features", "evaluate model…
Install Senior Data Scientist
$ npx skills add borghei/Claude-Skills --skill senior-data-scientist -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install borghei/Claude-Skills senior-data-scientist --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/borghei/Claude-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/engineering/senior-data-scientist .claude/skills/senior-data-scientist && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "senior-data-scientist" agent skill from https://github.com/borghei/Claude-Skills/tree/main/engineering/senior-data-scientist into .claude/skills/senior-data-scientist/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "senior-data-scientist", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/borghei/Claude-Skills/tree/main/engineering/senior-data-scientistType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add borghei/Claude-Skills --skill senior-data-scientist -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install borghei/Claude-Skills senior-data-scientist --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/borghei/Claude-Skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/engineering/senior-data-scientist .agents/skills/senior-data-scientist && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "senior-data-scientist" agent skill from https://github.com/borghei/Claude-Skills/tree/main/engineering/senior-data-scientist into .agents/skills/senior-data-scientist/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "senior-data-scientist", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add borghei/Claude-Skills --skill senior-data-scientist -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install borghei/Claude-Skills senior-data-scientist --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/borghei/Claude-Skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/engineering/senior-data-scientist .cursor/skills/senior-data-scientist && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "senior-data-scientist" agent skill from https://github.com/borghei/Claude-Skills/tree/main/engineering/senior-data-scientist into .cursor/skills/senior-data-scientist/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "senior-data-scientist", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/borghei/Claude-Skills.git --path engineering/senior-data-scientist--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add borghei/Claude-Skills --skill senior-data-scientist -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install borghei/Claude-Skills senior-data-scientist --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/borghei/Claude-Skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/engineering/senior-data-scientist .gemini/skills/senior-data-scientist && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "senior-data-scientist" agent skill from https://github.com/borghei/Claude-Skills/tree/main/engineering/senior-data-scientist into .gemini/skills/senior-data-scientist/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "senior-data-scientist", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install borghei/Claude-Skills senior-data-scientistInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add borghei/Claude-Skills --skill senior-data-scientist -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/borghei/Claude-Skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/engineering/senior-data-scientist .github/skills/senior-data-scientist && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "senior-data-scientist" agent skill from https://github.com/borghei/Claude-Skills/tree/main/engineering/senior-data-scientist into .github/skills/senior-data-scientist/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "senior-data-scientist", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add borghei/Claude-Skills --skill senior-data-scientist -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install borghei/Claude-Skills senior-data-scientist --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/borghei/Claude-Skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/engineering/senior-data-scientist .opencode/skills/senior-data-scientist && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "senior-data-scientist" agent skill from https://github.com/borghei/Claude-Skills/tree/main/engineering/senior-data-scientist into .opencode/skills/senior-data-scientist/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "senior-data-scientist", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
senior-data-scientist- GitHub stars
- 881
- Token cost
- ~1.7k tokens
- SKILL.md length
- 619 words
- Files
- 9 (incl. scripts, references)
- Skills in repo
- 349
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when the user asks to "design an experiment", "build a predictive model", "run A/B test analysis", "perform causal inference", "engineer features", "evaluate model…
- The user asks to design an experiment
- SKILL.md covers Keywords, Core Capabilities, When to Use and Clarify First, plus 4 more sections
- Runs Python scripts from its folder
- Build a predictive model
What it does
Senior Data Scientist is an agent skill from borghei/Claude-Skills. Use when the user asks to "design an experiment", "build a predictive model", "run A/B test analysis", "perform causal inference", "engineer features", "evaluate model performance", "set up MLOps pipeline", "analyze time series", "calculate sample size", or "deploy a model to production". Expert data science covering statistical modeling, experimentation, causal inference, feature engineering, ML deployment, and advanced analytics with Python, R, and SQL.
Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 10 other files, including scripts and reference files (for example `references/ds-operations.md`, `references/ds-workflows.md` and `references/experiment_design_frameworks.md`).
It sits in Data & Analytics, covering A/B testing, Machine learning and Experimental design. It works with Python and SQL. The repository describes itself as: 385 AI skills, 77 expert agents, and 900 stdlib Python tools for every team: engineering, PM, marketing, C-level, compliance, business ops, research, and a LinkedIn toolkit… The licence is MIT.
When your agent uses it
- The user asks to design an experiment
- Build a predictive model
- Run A/B test analysis
- Perform causal inference
Example prompts
- “design an experiment”
- “build a predictive model”
- “run A/B test analysis”
- “/senior-data-scientist”
Requirements
- Python 3
- Docker
What it can do on your machine
Read from SKILL.md and the folder at commit 4a698e8. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 3 files in scripts/ (Python), which the agent can run.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Senior Data Scientist loads about 1.7k tokens when it runs, and up to ~6k if it reads all its reference files. Until then it costs about 120 tokens; SKILL.md has 619 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
SKILL.md
The full file from borghei/Claude-Skills at commit 4a698e8, republished under its MIT licence (© borghei). 619 words, ~1,718 tokens.
Download SKILL.mdSave it as
.claude/skills/senior-data-scientist/SKILL.md (or your agent's skills folder). This skill also uses 8 other files; get the full folder from GitHub.- name
- senior-data-scientist
- description
- Use when the user asks to "design an experiment", "build a predictive model", "run A/B test analysis", "perform causal inference", "engineer features", "evaluate model performance", "set up MLOps pipeline", "analyze time series", "calculate sample size", or "deploy a model to production". Expert data science covering statistical modeling, experimentation, causal inference, feature engineering, ML deployment, and advanced analytics with Python, R, and SQL.
- license
- MIT + Commons Clause
- metadata.version
- 1.1.0
- metadata.author
- borghei
- metadata.category
- engineering
- metadata.domain
- data-science
- metadata.updated
- 2026-06-17
- metadata.tags
- data-science, ml, statistics, experimentation, python, mlops
Senior Data Scientist
Expert data science for statistical modeling, experimentation, ML deployment, and data-driven decision making — A/B test design and analysis, feature engineering, model training/evaluation, production deployment, and causal inference.
Keywords
data-science, machine-learning, statistics, a-b-testing, causal-inference, feature-engineering, mlops, experiment-design, model-deployment, python, scikit-learn, pytorch, tensorflow, spark, airflow
Core Capabilities
- Experiment design & analysis — hypothesis framing, power analysis and sample sizing, randomization, SRM monitoring, and post-hoc significance testing.
- Feature engineering — profiling, candidate generation (temporal/aggregation/interaction/text), selection (variance, correlation, SHAP/RFE), and leakage validation.
- Model training & evaluation — stratified/temporal splits, baselines, hyperparameter tuning, cross-validation, calibration, and fairness checks.
- Production deployment — containerized serving, input/output drift monitoring (KS/PSI), canary rollouts, and latency/error SLAs.
- Causal inference — propensity score matching, difference-in-differences, regression discontinuity, instrumental variables, and assumption/placebo testing.
When to Use
- Designing or analyzing an A/B test.
- Building a feature engineering pipeline.
- Training, evaluating, or deploying an ML model.
- Estimating treatment effects from observational data.
Clarify First
Before running an analysis or pipeline, confirm these inputs. If any is unknown or vague, ASK — do not assume:
- Task — A/B test design / feature engineering / model evaluation / causal inference (selects the script and workflow)
- Dataset & target variable — what you are modeling or measuring (drives feature generation and leakage validation)
- Decision metric & minimum effect — the metric and the smallest effect worth detecting (drives power analysis and sample size)
Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.
Tools
| Script | Purpose |
|---|---|
scripts/experiment_designer.py | A/B test design, power analysis, sample size calculation |
scripts/feature_engineering_pipeline.py | Automated feature generation, correlation analysis, feature selection |
scripts/model_evaluation_suite.py | Model comparison, cross-validation, deployment readiness checks |
For hypothesis testing, causal inference, and regression analysis, use inline scipy/statsmodels — see references/statistical_methods_advanced.md.
References
Load the reference that matches the task — keep this file lean and pull detail on demand:
- references/ds-workflows.md — quick-start commands, tech stack, the five end-to-end workflows (A/B testing, feature pipeline, train/evaluate, deploy, causal inference) with Python snippets, performance targets, and common commands. Read when executing any data-science task.
- references/ds-operations.md — troubleshooting table, success criteria, and the full CLI flag reference for each script. Read when diagnosing issues or running the tools.
- references/statistical_methods_advanced.md — advanced statistical methods reference (hypothesis testing, causal inference, regression). Read for statistical depth.
- references/experiment_design_frameworks.md — experiment design frameworks and power-analysis foundations. Read when designing rigorous experiments.
- references/feature_engineering_patterns.md — feature engineering patterns and selection techniques. Read when building features.
Show full SKILL.md (230 more words)Show less
Scope & Limitations
This skill covers:
- End-to-end experiment design including power analysis, randomization, and post-hoc analysis
- Feature engineering pipelines with profiling, generation, selection, and validation
- Model training evaluation including cross-validation, calibration, and fairness checks
- Production model deployment with monitoring, drift detection, and canary rollouts
This skill does NOT cover:
- Data engineering infrastructure (ETL orchestration, pipeline scheduling, data lake management) -- see
senior-data-engineer - Deep learning model architecture design and training at scale (distributed GPU training, custom layers) -- see
senior-ml-engineer - Prompt engineering, RAG systems, and LLM fine-tuning workflows -- see
senior-prompt-engineer - Computer vision pipelines (object detection, segmentation, video processing) -- see
senior-computer-vision
Integration Points
| Skill | Integration | Data Flow |
|---|---|---|
senior-data-engineer | Feature pipeline ingests data from ETL outputs; shares data quality validation patterns | Raw data stores --> feature engineering pipeline --> feature store |
senior-ml-engineer | Trained models handed off for MLOps deployment; shares model registry and serving configs | Evaluated model artifacts --> deployment pipeline --> production serving |
senior-prompt-engineer | Embedding features from LLMs feed into ML pipelines; experiment frameworks apply to prompt A/B tests | LLM embeddings --> feature vectors; experiment designs --> prompt evaluation |
senior-architect | Model serving architecture reviewed for scalability; data platform design aligned with training infrastructure | Architecture specs --> deployment topology --> monitoring dashboards |
senior-backend | Model inference endpoints integrated into backend services; API contracts defined for prediction requests | REST/gRPC model API --> backend service layer --> client applications |
senior-devops | CI/CD pipelines extended for model retraining triggers; containerized model images deployed via infrastructure-as-code | Docker images --> Kubernetes manifests --> production clusters |
© borghei, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 8 other files (scripts, references) in engineering/senior-data-scientist of borghei/Claude-Skills.
- SKILL.md
- references/ds-operations.md
- references/ds-workflows.md
- references/experiment_design_frameworks.md
- references/feature_engineering_patterns.md
- references/statistical_methods_advanced.md
- scripts/experiment_designer.py
- scripts/feature_engineering_pipeline.py
- scripts/model_evaluation_suite.py
Open the folder on GitHubat commit 4a698e8
Compare with similar skills
Senior Data Scientist next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Senior Data Scientist this skillborghei/Claude-Skills | 881 | — | ~1.7k | Automated safety check: Pass | MIT | |
| Senior Data ScientistRaidriar7170/hermes-skilleval | 125 | 6 repos | ~1.4k | Automated safety check: Pass | MIT | |
| Senior Data Scientistalirezarezvani/claude-skills | 28k | 2 repos | ~2.3k | Automated safety check: Pass | MIT | |
| Mle Workflowaffaan-m/ECC | 275k | 1 repos | ~5.6k | Automated safety check: Pass | MIT | |
| Power Analysisgaasher/Agent-Loop-Skills | 174 | — | ~2.2k | Automated safety check: Pass | MIT | |
| Time Series Analytics Useropen-edge-platform/edge-ai-libraries | 169 | — | ~3.1k | Automated safety check: Pass | Apache-2.0 |
Similar skills
Senior Data Scientist
alirezarezvani/claude-skills
World-class senior data scientist skill specialising in statistical modeling, experiment design, causal inference, and predictive analytics.
Mle Workflow
affaan-m/ECC
Production machine-learning engineering workflow for data contracts, reproducible training, model evaluation, deployment, monitoring, and rollback.
Power Analysis
gaasher/Agent-Loop-Skills
A skill your agent uses when the user is planning a two-arm comparison (an A/B test, a simple RCT, a behavioral study, or a two-model/two-config evaluation) and needs to size it and preregister it…
Time Series Analytics User
open-edge-platform/edge-ai-libraries
Build a new time-series analytics use case on top of the deployed Time Series Analytics microservice — bring it up with Docker Compose (from a repo clone, or by fetching the compose files from…
Aeon Time Series Machine Learning
davila7/claude-code-templates
Guides time series machine learning with the aeon toolkit: classification, regression, clustering, forecasting, anomaly detection, segmentation and similarity search.
More from borghei/Claude-Skills
All 349 skills in this repoAgents In The Team
borghei/Claude-Skills
Run delivery when AI coding and ops agents take tickets. An agent skill from borghei/Claude-Skills.
AI Content Disclosure
borghei/Claude-Skills
Check AI-generated marketing content and reviews for required disclosures under the EU AI Act, FTC rules and platform AI-label policies.
AI Prototyping
borghei/Claude-Skills
Idea to AI-generated prototype to customer validation to engineering handoff.
Analytics Engineer
borghei/Claude-Skills
Analytics engineering across data modeling, dbt, transformation, and semantic layers.
Ansoff Matrix
borghei/Claude-Skills
Ansoff Matrix — 4-quadrant framework for growth options: market penetration, market/product development, and diversification.
Brainstorm Okrs
borghei/Claude-Skills
OKR brainstorming and validation using the Radical Focus framework — outcome objectives, measurable key results, counter-metrics.
Related
Questions about Senior Data Scientist
What does Senior Data Scientist do?
A skill your agent uses when the user asks to "design an experiment", "build a predictive model", "run A/B test analysis", "perform causal inference", "engineer features", "evaluate model…. Senior Data Scientist is an agent skill from borghei/Claude-Skills. Use when the user asks to "design an experiment", "build a predictive model", "run A/B test analysis", "perform causal inference", "engineer features", "evaluate model performance", "set up MLOps pipeline", "analyze time series", "calculate sample size", or "deploy a model to production".
When should I use Senior Data Scientist?
Senior Data Scientist fits situations like: the user asks to design an experiment; build a predictive model; run A/B test analysis; perform causal inference.
How do I install Senior Data Scientist in Claude Code?
Run `npx skills add borghei/Claude-Skills --skill senior-data-scientist -a claude-code`. Or copy the skill folder (engineering/senior-data-scientist in borghei/Claude-Skills) into .claude/skills/senior-data-scientist in your project. Claude Code loads it when a task matches its description.
How do I install Senior Data Scientist in Codex?
Run `npx skills add borghei/Claude-Skills --skill senior-data-scientist -a codex`. Or copy the skill folder (engineering/senior-data-scientist in borghei/Claude-Skills) into .agents/skills/senior-data-scientist in your project. Codex loads it when a task matches its description.
Can I use Senior Data Scientist in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add borghei/Claude-Skills --skill senior-data-scientist -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/senior-data-scientist, .gemini/skills/senior-data-scientist, .github/skills/senior-data-scientist and .opencode/skills/senior-data-scientist in your project.
What does Senior Data Scientist need to run?
Going by SKILL.md and its folder, Senior Data Scientist needs Python for the scripts in its folder. Our summary lists: Python 3; Docker.
Does Senior Data Scientist access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Senior Data Scientist safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Senior Data Scientist use?
Senior Data Scientist is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Senior Data Scientist use?
About 1.7k tokens (SKILL.md is roughly 6.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 4.3k tokens, read only when the agent opens those files.
What are the alternatives to Senior Data Scientist?
Skills that share tags, products or a category with Senior Data Scientist: Senior Data Scientist (Raidriar7170/hermes-skilleval, 125 stars), Senior Data Scientist (alirezarezvani/claude-skills, 28k stars), Mle Workflow (affaan-m/ECC, 275k stars) and Power Analysis (gaasher/Agent-Loop-Skills, 174 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Senior Data Scientist?
borghei (a GitHub user) maintains it in borghei/Claude-Skills, which has 881 GitHub stars. The repository holds 349 skills in this directory. The repository was last updated on October 7, 2026.
Source: borghei/Claude-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.