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Clariant

Data Scientist (m/f/d)

Clariant Łódź, Łódź Voivodeship, Poland · PLN 176K–PLN 292K/yr

Chemical Manufacturing · 10,001+ employees

Sep 14
data-scientist Senior (5-10 yrs) Full-time Poland
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About the role

Lead and manage data science projects from planning through execution to drive digitalization and business impact. Collaborate cross-functionally to develop data products that integrate modern tools into existing chemical industry processes.

What they look for

Data Science Optimization Techniques Digitalization Process Knowledge Data Analysis Automation Data Products Project Management Industrial Processes Chemical Industry

Requirements

Requires a strong understanding of industrial processes and the ability to interpret data patterns within an operational context. Candidates must be capable of driving data science adoption and scaling solutions across the organization.

Full description

Job ID: 41833 | Location: Lodz, Poland | Expected Salary Range: zł175,500.00 - zł292,500.00 | Work Model: Hybrid

Are you passionate about transforming data into real business impact in the chemical industry? As a Data Scientist at Clariant, you will drive digitalization projects by applying advanced data science and optimization techniques to improve products and processes. You will accelerate the use of data across the organization to enable smarter automation, better decision-making, and innovative data-powered business models. Join us and become a key force in shaping Clariant's digital future.

What will you be doing?

  • Lead and manage data science projects from planning through execution, ensuring delivery within scope, budget, and quality standards.
  • Apply process knowledge to interpret results meaningfully and develop solutions that are grounded in real-world operational context.
  • Connect data patterns with chemical processes — a strong understanding of industrial processes is key to making the real difference.
  • Collaborate cross-functionally to develop complete data products that address real business challenges.
  • Drive data science adoption across the organization.
  • Integrate modern data science tools into existing systems and scale solutions effectively.
  • Develop internal data science capabilities while fostering external partnerships for continuous innovation.

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