Data Scientist

Danaher Corporation

1d•$74,000 - $111,000•Remote

About The Position

The Data Scientist is responsible for developing and applying statistical, machine learning, and advanced analytical solutions to support data-driven decision-making. This role works closely with data engineering, business intelligence, and functional stakeholders to translate business questions into analytical models and insights. The position focuses on applied analytics and machine learning, supporting the development, validation, and use of models in enterprise environments. The Data Scientist applies strong analytical rigor and practical modeling skills to ensure results are accurate, interpretable, and actionable for business stakeholders. This position reports to the Senior Manager, BI and Analytics and is part of the Information Technology Department and will be fully remote.

Requirements

Bachelor’s or master’s degree in a quantitative field with 2+ years of hands-on experience in data analysis, data science, or applied analytics.
Proficiency in Python and SQL, working knowledge of statistical methods and machine learning techniques, and familiarity with the end-to-end analytics lifecycle from data preparation through validation and interpretation.
Strong problem-solving skills with the ability to clearly explain analytical results, translate insights into business value, and collaborate effectively in a cross-functional environment

Nice To Haves

experience with time-series forecasting, optimization, or anomaly detection is a plus

Responsibilities

Develop and maintain analytical models: Apply statistical and machine learning techniques (e.g., regression, classification, time series) to build, validate, monitor, and improve models that support business forecasting and decision-making.
Execute the end‑to‑end analytics lifecycle: Perform data preparation, exploratory and statistical analysis, feature engineering, model validation, and result interpretation using enterprise-scale data platforms.
Leverage core technical tools and data platforms: Use Python (pandas, NumPy, scikit‑learn) and SQL to analyze structured and semi‑structured data, work with relational databases, and support production-ready analytical solutions.
Translate analytics into business insight: Partner with stakeholders to define analytical questions and success metrics, clearly communicate findings, and integrate outputs into reports, dashboards, or downstream systems.
Collaborate, document, and continuously improve: Work effectively in cross‑functional teams, document methodologies and assumptions, contribute to shared code and best practices, and stay current on evolving analytical techniques and tools.