skills.rest
Fantasia1999
Fantasia1999COMMUNITY
Explore all skills by this author →
WebsiteGitHub

senior-data-scientist

Design A/B tests, build feature pipelines, and train predictive models with MLflow tracking.

Updated Mar 12, 2026
Search Tags:#mlops#data science#machine learning#feature engineering#statistical modeling#causal inference#a/b testing
One-click install
npx skills add https://github.com/Fantasia1999/claude-skills-zh --skill senior-data-scientist-fantasia1999
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: senior-data-scientist
Source: https://github.com/Fantasia1999/claude-skills-zh/tree/main/translations/engineering-team/senior-data-scientist
Command: npx skills add https://github.com/Fantasia1999/claude-skills-zh --skill senior-data-scientist-fantasia1999

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill empowers users to design, build, and analyze sophisticated data-driven models and experiments, translating complex statistical concepts into actionable business insights.

Core Features & Use Cases

  • Experiment Design & Analysis: Design A/B tests, calculate sample sizes, and analyze results with statistical rigor (e.g., z-tests, Bonferroni correction).
  • Feature Engineering: Construct robust feature pipelines for structured data using scikit-learn and time-based features.
  • Model Training & Evaluation: Train and evaluate predictive models (classification/regression) using cross-validation and track experiments with MLflow.
  • Causal Inference: Apply methods like Difference-in-Differences (DiD) to estimate causal effects from observational data.
  • Use Case: A product manager wants to test a new feature's impact on conversion rates. This Skill can help them calculate the required sample size, set up the experiment, analyze the results, and ensure statistical validity.

Quick Start

Use the senior-data-scientist skill to design an A/B test for a new signup flow, aiming for a 5% lift in conversions with 90% power and a 5% significance level.

Frequently Asked Questions about senior-data-scientist

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I calculate sample size for an A/B test to ensure statistical validity?▼

To calculate A/B test sample size, you define expected lift, statistical power, and significance level. This Skill computes required sample sizes and analyzes results using z-tests and Bonferroni correction for rigorous experimental design.

How do I build feature engineering pipelines for time-series data using scikit-learn?▼

Building feature engineering pipelines with scikit-learn involves transforming structured data and extracting time-based features. This Skill constructs robust pipelines to process temporal data and prepare it for predictive model training.

Can I track machine learning model training and evaluation using MLflow?▼

Yes, you can track machine learning model training and evaluation using MLflow. This Skill trains classification and regression models with cross-validation and utilizes MLflow to log and track experiment metrics seamlessly.

What is the best way to estimate causal effects from observational data?▼

Estimating causal effects from observational data is best achieved using Difference-in-Differences with OLS regression. This Skill applies causal inference techniques to estimate the true impact of interventions on your data.

Does this Skill support XGBoost and Pandas for statistical modeling?▼

Yes, this Skill supports XGBoost and Pandas for statistical modeling. It utilizes Python libraries including NumPy, Pandas, Scikit-learn, XGBoost, and Statsmodels to execute advanced data science tasks.

Related Skills

Explore More Data & Analytics Skills→
KaranKathur06KaranKathur06

data-science-expert

Convert raw tabular and time-series data into modeling-ready datasets with statistical analysis.

Community
Advanced
Phife726Phife726

ds-mlops-deployment

Deploy trained ML models to production with monitoring and drift detection.

Community
Advanced
pluginagentmarketplacepluginagentmarketplace
5

machine-learning

Automate end-to-end machine-learning workflows with scikit-learn and XGBoost.

Community
Intermediate
olavocarvalhoolavocarvalho
1

ab-test

Analyze A/B test data with Frequentist and Bayesian significance testing.

Community
Advanced