Skip to content
TrustedSkillsbeta
Back to all skills

Senior Data Scientist

Checked
ovachiever·vlatest·data

Analyzes complex datasets, builds predictive models, and delivers data-driven insights to optimize business outcomes.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add ovachiever/droid-tings --skill senior-data-scientist -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

ovachiever

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned ovachiever/droid-tings at 7acd12a754.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
7acd12a754
Manifest
skills/senior-data-scientist/SKILL.md
Scanned
7 files, 769 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill provides access to a suite of tools and expertise emulating that of a senior data scientist, focused on building and deploying production-grade AI/ML systems. It enables an agent to design experiments, engineer features, evaluate models, and implement best practices for scalable and optimized data solutions. The skill leverages Python scripts and configuration files to automate key processes within the machine learning lifecycle.

When to use it

  • When needing assistance with designing and implementing machine learning experiments.
  • To optimize feature engineering pipelines for improved model performance.
  • For evaluating and deploying machine learning models into production environments.
  • When requiring guidance on MLOps and DataOps best practices at scale.

Key capabilities

  • Experiment design using experiment_designer.py
  • Feature engineering pipeline analysis with feature_engineering_pipeline.py
  • Model evaluation and deployment via model_evaluation_suite.py
  • Expertise in advanced production patterns, scalable system design, performance optimization, MLOps/DataOps, real-time processing, distributed computing, model deployment & monitoring, security, cost optimization, team leadership, and mentoring.
  • Proficiency across a wide range of technologies including Python, SQL, R, Scala, Go, PyTorch, TensorFlow, Spark, Airflow, dbt, Kafka, Databricks, LangChain, LlamaIndex, DSPy, Docker, Kubernetes, AWS/GCP/Azure, MLflow, Weights & Biases, Prometheus, PostgreSQL, BigQuery, Snowflake, and Pinecone.

Example prompts

  • "Design an experiment to test the impact of feature X on model Y."
  • "Analyze this dataset and suggest features for a predictive model."
  • "Evaluate the performance of my existing machine learning model and prepare it for deployment."

Tips & gotchas

  • The skill relies on Python scripts, so ensure the agent has access to a suitable Python environment.
  • Familiarity with data science concepts and terminology will improve interaction effectiveness.
  • Refer to the reference documentation (references/statistical_methods_advanced.md, references/experiment_design_frameworks.md) for more detailed guidance on specific techniques.