Process Systems Engineer

Eli Lilly Eli Lilly · Pharma · Indianapolis, IN

Seeking a Process Systems Engineer to apply computerized model-based solutions to accelerate the development of new medicines. Responsibilities include designing and optimizing pharmaceutical manufacturing processes using modeling, simulation, and data analysis, developing computational tools, and training others. The role involves collaborating with multidisciplinary teams, managing external manufacturing networks, and ensuring compliance with regulations.

What you'd actually do

  1. Apply process systems engineering fundamentals towards designing and optimizing pharmaceutical manufacturing processes including but not limited to solids operations, chemical reactions, separation processes, bioprocessing (upstream/downstream) using modeling, simulation and optimization tools in conjunction with data analysis.
  2. Design appropriate strategies to apply a variety of computational tools to support the development and scale-up of pharmaceutical manufacturing processes.
  3. Implement and execute the necessary data analysis, models and/or simulations to support to the design or scale-up of pharmaceutical manufacturing processes.
  4. Analyze and interpret data collected from laboratory experiments and/or from manufacturing (including parameter estimation).
  5. Actively participate in the design of process conditions for process understanding (experiments) or for material production in manufacturing (nominal conditions).

Skills

Required

  • process systems engineering fundamentals
  • modeling
  • simulation
  • optimization tools
  • data analysis
  • thermodynamics
  • material characterization techniques
  • numerical methods
  • optimization
  • transport phenomena
  • Python
  • MATLAB
  • gPROMS
  • Aspen +
  • GAMS
  • Pyomo
  • R

Nice to have

  • process systems engineering
  • building fundamental, empirical or hybrid models
  • simulation tools across scales (DEM,CFD, FEM, PBM etc)
  • parameter estimation
  • handling of data uncertainty
  • techno economic evaluations
  • supply chain
  • production planning
  • operations research related modeling and optimization
  • data generating sources
  • automation architectures
  • exemplary teamwork/interpersonal s

What the JD emphasized

  • strong technical depth
  • scientific leadership
  • compliance with regulations, guidelines, procedures, and practice governing drug research and development