Data Scientist - Danaher

Bring more to life.

Are you ready to accelerate your potential and make a real difference within life sciences, diagnostics and biotechnology?

At Phenomenex, one of Danaher ’s 15+ operating companies, our work saves lives—and we’re all united by a shared commitment to innovate for tangible impact.

You’ll thrive in a culture of belonging where you and your unique viewpoint matter. And by harnessing Danaher ’s system of continuous improvement, you help turn ideas into impact – innovating at the speed of life.

Phenomenex isn’t your typical scientific company. Founded nearly 40 years ago, Phenomenex is a global technology leader committed to developing novel analytical chemistry solutions that solve the separation and purification challenges of researchers, advancing the future of scientific analysis and investigation, ensuring the quality of essentials like your food, water, shampoo, and even cold medication. Be part of our global success and together, we accelerate the discovery, development and delivery of solutions that safeguard and improve human health.

Learn about the Danaher Business System which makes everything possible.

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.

In this role, you will have the opportunity to:

• 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 downstr