Position as Data Scientist (m/f/d) – Pilot Project Predictive LCA Model at MaxPlanckInstitut fr Kolloid und Grenzflchenforschung
14476 Potsdam, Brandenburg, Germany -
Full Time


Start Date

Immediate

Expiry Date

13 Jun, 25

Salary

0.0

Posted On

13 Mar, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Code, Machine Learning, Life Cycle Assessment, Processing, Ownership

Industry

Information Technology/IT

Description

Online since 2025-03-12Position as Data Scientist (m/f/d) – Pilot Project Predictive LCA Model
Deadline
31.03.2025
Start of work
as soon as possible
Working time model
Full time
About us
The Center for the Transformation of Chemistry (CTC) aims to reimagine the chemical industry as sustainable and resilient. Divided into 5 interconnected thematic areas; Automation & Standardization, Data-driven Chemistry, Recycling, Renewable Feedstocks, and Societal-Environmental-Economic Metrics (SEEM); the CTC has identified strong pillars to guide its scientific efforts towards the transformation of chemistry.
The CTC Science Team is currently planning multiple pilot projects and looking for ambitious people taking the lead.
As a Data Scientist (m/f/d) – Pilot Project Predictive LCA Model, you will play a crucial role in advancing sustainability through the development of a predictive life cycle assessment (LCA) model by using machine learning techniques, including Multiple Regression and Artificial Neural Networks (ANN), you will design, develop, and implement a model that significantly enhances our understanding of environmental impacts for lignin-derived chemicals based on the molecular structure.
Your responsibilities will include benchmarking the developed predictive LCA model against existing ones to evaluate their accuracy in assessing the environmental impact of lignin-derived chemicals. By doing so, you will provide critical insights that drive our LCA processes forward, contributing to more sustainable chemical practices. Your expertise will be instrumental in ensuring that our models not only predict environmental impacts accurately but also support the broader goal of sustainability at CTC.
As part of CTC’s Science team, you’ll be located in Leuna in the greater Leipzig/Halle area.

Your tasks and qualifications

  • Formulation and implementation of productive and scalable machine learning and deep learning solutions related to predictive Life Cycle Assessment.
  • Analyze, interpret, and report complex LCA datasets from Ecoinvent in a clear and actionable format for Scientific coordinators and the Project Leader.
  • Coordinate with data engineers to deploy code, have experience with CI/CD pipelines, and have experience with logging tools such as Prometheus Grafana and Docker containers for development.
  • Ownership of ML models from conceptualization to production-level implementation.
  • Collaborate with the Data team on Data standardization, wrangling, and processing

Your profile

  • Successfully completed degree (at least a Master’s, ideally PhD) in Computer Science, Industrial Ecology Engineering, Data Science, Statistics, Computational Sciences, or related fields.
  • Previous hands-on working experience (minimum 5 years) as a data scientist, AI developer, or similar role.
  • Experience with NoSQL databases (e.g. PostgreSQL, MySQL).
  • Previous experience in LCA modelling through commercial Software (e.g. SimaPro or Gabi), especially for the chemical industry.
  • Strong experience in programming languages such as Python and VSCode.
  • A hands-on expertise with version control systems (e.g. Git), ETL tools (e.g. Airflow, Nifi), and high-performance computing will be advantageous.
  • Experience working with relational and non-relational databases and data streaming frameworks (Apache Kafka/Spark/SQL).
  • Motivated by working in a self-organized and independent fashion within an international and interdisciplinary environment.
  • Excellent communication, collaboration, and leadership skills to engage with internal and external stakeholders.

What we offer

  • The opportunity to perform your research at what will be the largest research center for chemical research in Europe.
  • Benefit from a large network: Meet and work with top experts from academia and industry.
  • Competitive compensation according to TVöD-Bund up to E13 depending on the candidate’s qualifications.
  • Partial compensation of Job Commute Ticket.
  • Comprehensive social benefits, including 30 days of vacation per year, annual special payment, and additional pension scheme (VBL).
  • Term-limited position until the 31st of December, 2025 allowing for fresh perspectives in terms of contract extension after 2025.

The Max Planck Society strives for gender equality and diversity. We welcome applications from all backgrounds. The Max-Planck Society is committed to increasing the number of individuals with disabilities in its workforce and therefore encourages applications from such qualified individuals. Furthermore, the Max Planck Society seeks to increase the number of women in those areas where they are underrepresented and therefore explicitly encourages women to apply.
The position is available starting immediately after the offer is awarded.
To apply for the position of Data Scientist (m/f/d) at the CTC in, please submit your CV and a concise overview of relevant experience via our online application management tool.

Responsibilities
  • Formulation and implementation of productive and scalable machine learning and deep learning solutions related to predictive Life Cycle Assessment.
  • Analyze, interpret, and report complex LCA datasets from Ecoinvent in a clear and actionable format for Scientific coordinators and the Project Leader.
  • Coordinate with data engineers to deploy code, have experience with CI/CD pipelines, and have experience with logging tools such as Prometheus Grafana and Docker containers for development.
  • Ownership of ML models from conceptualization to production-level implementation.
  • Collaborate with the Data team on Data standardization, wrangling, and processin
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