Engineer – Data‑Driven Simulation & Machine Learning

About the role

The Engineer will apply advanced simulation and data‑driven methods to solve complex filtration and separation challenges within Pall’s R&D organization. Working at the intersection of computational fluid dynamics, machine learning and modern data platforms, you will help turn experimental and simulation data into actionable insights that accelerate product development.

Key responsibilities

• Develop predictive models and machine‑learning solutions using Python or similar languages.
• Run CFD simulations (e.g., Ansys Fluent) to generate product and process insights.
• Collect, structure, and analyse experimental, simulation and operational data ensuring high quality and consistency.
• Design and scale R&D data infrastructure on platforms such as Databricks or Snowflake, including ETL pipelines and data‑governance implementation.
• Validate and refine models against real‑world data, performing statistical analysis and sensitivity studies.
• Collaborate closely with product development engineers, experimental scientists and digital stakeholders.

Required profile

• Strong analytical mindset with experience in CFD, data science or both.
• Ability to work cross‑functionally and translate technical results into clear recommendations.
• Proficiency in programming (Python) and familiarity with modern data‑engineering tools.

Required skills

• Python programming
• Ansys Fluent (CFD)
• Databricks
• Snowflake
• ETL pipeline development
• Machine‑learning model building
• Statistical analysis