At BASF Digital Hub Madrid we develop innovative digital solutions for BASF, create new exciting customer experiences and business growth, and drive efficiencies in processes, helping to strengthen BASF´s position as the digital leader in the chemical industry. We believe the right path is through creativity, competence and great people working and learning together. Become part of our team and develop the future with us – in a global team that embraces diversity and equal opportunities.

What you can expect:
Our team “Artificial Intelligence (AI) Solutions for Supply and Value Chains” develops innovative solutions that support better and faster business decisions. We implement data-driven solutions in its complete lifecycle for users in supply chain management around the world.

Your main responsibilities:

• You will be responsible for implementing AI solutions such as mathematical optimization for decision support along value chains.
• You will apply and enhance optimizers of standard planning software for supply chain planning (e.g., SAP APO).
• You will further develop non-standard optimizers for scheduling and sizing of batch production to be integrated into the standard planning software.
• You will have a strong focus on data engineering and on the further development of the existing middleware to connect the non-standard optimizers with the standard planning software.
• You will guide and train new user teams in understanding data needs, data quality, mathematical optimization, and its relevance for value-based planning processes.
• You will translate requirements of business functions like supply chain, marketing and production into AI solutions including mathematical optimization.
• You will contribute to benefit cases and guide business partners through the process of selecting, comparing, and interpreting scenarios of optimization runs.
• You will improve our standard planning software with regards to user interfaces, mathematical modelling, data quality management and adoption to business needs.

Our main requirements:

• A Master degree in the field of information technology studies or an (industrial) engineering degree in IT, computer science, statistics, mathematics, operations research, physics or related discipline with at least 2-3 years of experience in a similar role.
• You have a high interest in applying quantitative methods and in developing sustainable digital business solutions.
• Hands-on coding skills in relevant languages, like GAMS, Python, C#, SQL
• At least 2 years of experience in applying mathematical optimization in an industrial setting or Experience with Supply Chain Planning software, e. g. SAP APO or OMP
• Business consulting and technical consulting skills.
• An innovation minded and entrepreneurial spirit and the ability to contribute to a positive and energized culture.
• A growth mindset with a curiosity to learn and improve.
• Team player with strong interpersonal, written, and verbal communication skills.
• You can demonstrate fluent communication skills in English (spoken and
  written).

Nice to have:

• Experience in supply chain management / planning.
• Experience with data visualization software such as Power BI, Tableau.
• Experience with Enterprise Resource Planning software, e. g. SAP.
• German language skills (spoken and written).

What’s in it for you:

• A secure work environment because your health, safety and wellbeing is always our top priority.
• Flexible work schedule and Home-office options, so that you can balance your working life and private life.
• Learning and development opportunities.
• 30 holiday days per year.
• A collaborative, trustful and innovative work environment.
• Being part of an international team and work in global projects.
• Relocation assistance to Madrid provided.
• Working on cutting edge technologies on exciting digitalization projects in the area of Artificial Intelligence.
• Joining an inter-disciplinary team with end-to-end responsibility in value chain
  optimization.
• Being part of a truly global team, which is living inclusion by gender, age,
  education and cultural background.