Data Science Trainee

About the role

We are the world leader in stone wool solutions. Founded in 1937 in Denmark, we transform volcanic rock into safe, sustainable products that help people and communities thrive. We are a global company with more than 12,200 employees, located in 40+ countries with 51 manufacturing facilities... all focused on one common purpose - to release the natural power of stone to enrich modern living. Sustainability is central to our business strategy. ROCKWOOL was one of the first companies to commit to actively contributing to the United Nations Sustainable Development Goals (SDGs) framework and are actively committed to 11 SDGs, including SDG 14, Life Below Water. Through our partnership with the One Ocean Foundation and in connection with our sponsorship of the ROCKWOOL Denmark SailGP team, we will help raise awareness around ocean health challenges in an effort to accelerate solutions to protect it. Diverse and inclusive culture: We want all our people to feel valued, respected, included and heard. We employ 79 different nationalities worldwide and are committed to providing equal opportunities to all employees, promote diversity, and work against all forms of discrimination among ROCKWOOL employees. At ROCKWOOL, you will experience a friendly team environment. Our culture is very important to us. In fact, we refer to our culture as "The ROCKWOOL Way". This is the foundation in which we operate and is based

Responsibilities

• We are looking for a motivated trainee with an interest in machine learning and data analysis. During the internship, you will work on researching and tuning models for predicting water levels and water drops in hydroponic cultivation systems - a key aspect of optimizing our solutions.
• Main responsibilities:
• Research and development of predictive models
• Testing various architectures: neural networks, ARIMA, D-Linear, and decision trees (previously implemented solutions should be tested on different datasets and the results should be analyzed and documented)
• Optimizing model parameters to maximize prediction accuracy
• Analysis of external factors
• Evaluating model accuracy when adding additional parameters
• Assessing model accuracy by substrate type and crop type
• Automation and implementation
• Running multiple analytical experiments for different cultivation scenarios
• Building algorithms based on predictive modeling results
• Cloud infrastructure
• Working with ML hosting tools on the AWS platform (SageMaker, EC2, S3)
• What you bring:
• Current student status
• Completed at least 3 years of higher education (Computer Science, Mathematics, Engineering, Data Science, or a related field)
• Availability around 20-35 hours per week
• Knowledge of Python (numpy, pandas, scikit-learn)
• Basic knowledge of machine learning and statistics
• Understanding of methodologies for building and evaluating predictive models
• English at a minimum B2 level

Requirements

• Experience with AWS (SageMaker, EC2, Lambda, S3)
• Familiarity with deep learning libraries (TensorFlow, PyTorch)
• Experience with time series models (ARIMA, Prophet)
• Knowledge of Git and code versioning
• Experience in IoT or sensor-related projects

Benefits

Additional Information

We are looking for a Data Science Trainee to be based in our Poznań Office. Ready to help build a better future for generations to come? In an ever-changing, fast paced world, we owe it to ourselves and our future generations to live life responsibly. At ROCKWOOL, we work relentlessly to enrich modern living through our innovative stone wool solutions. Join us and make a difference! Your future team: The Solutions & Marketing is a dedicated group of professionals focused on developing and maintaining the e-Gro application, designed to optimize agricultural processes through advanced technology and data analytics. Our team encompasses a diverse range of expertise, including software development, project management, cloud computing, customer support, and marketing. The Solutions & Marketing team is committed to leveraging technology to enhance agricultural productivity and sustainability, providing cutting-edge solutions to modern farming challenges.

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