Senior Analyst - Data Science

Overview

We’re Looking For a Data Science Professional Who

• Translates complex datasets into clear, compelling narratives for stakeholders.
• Designs and implements high-quality, reproducible models with lasting business impact.
• Champions analytical rigor, documentation excellence, and solution scalability.
  

• Frame ambiguous workforce problems into testable hypotheses; choose fit‑for‑purpose methods and clearly communicate assumptions and limitations.
• Apply statistical techniques like hypothesis testing, regression, causal inference, survival/time‑series, and ensemble methods to drive actionable recommendations.
• Use ML/NLP across structured and unstructured HR data (e.g., text from surveys or talent profiles); design A/B or quasi‑experiments where appropriate.
  

• Develop production‑ready pipelines and models in Python/R with SQL; publish decision‑ready visuals in Power BI (or equivalent).
• Establish reproducibility (versioning, environments, modular code) and monitoring (drift, performance, fairness) with clear runbooks and documentation.
• Partner with data engineering on ETL/ELT and analytics engineering best practices to ensure scalability and reliability.
  

• Handle HR data with care: follow Eaton’s Data Protection standards for PII/SPII, privacy‑by‑design, access controls, and model transparency.
  

• Co‑create problem statements, success metrics, and acceptance criteria with HR and business leaders; communicate insights for non‑technical audiences.
• Connect insights into decisions and outcomes (e.g., quality of hire, retention, productivity, org health); quantify impact and document learning.
• Lead medium‑to‑large analytics projects with minimal oversight; coach peers on methods and code quality; contribute re‑usable components and standards.
  

Qualifications

• Bachelor’s degree in Statistics, Data Science, Engineering, Computer Science, or related field or equivalent practical experience; master’s preferred.
• 6-8 years of applied data science/advanced analytics, ideally in People/Workforce Analytics or adjacent domains with sensitive data.
  

Skills

• Proficiency in Python (pandas, scikit‑learn/statsmodels), R, and dashboards in Power BI (or similar).
• Strong grounding in statistical modeling, causal inference, experimental design, and time‑series.
• Demonstrated ability to translate complex analyses into clear recommendations for senior stakeholders.
• Excellent communication and storytelling.