Application Guide

How to Apply for Data Science Engineer (f/m/d)

at Prewave

🏢 About Prewave

Prewave is a fast-growing Austrian tech startup that leverages AI to transform supply chain management, focusing on ESG compliance and sustainability. By joining Prewave, you'll work on cutting-edge NLP solutions that directly impact global supply chain transparency and ethical sourcing.

About This Role

As a Data Science Engineer, you'll develop and deploy NLP models to extract insights from diverse datasets, enhancing supply chain resilience. Your work will involve fine-tuning Torch and TensorFlow models and ensuring they perform reliably in production, directly influencing real-world business decisions.

💡 A Day in the Life

You'll start by reviewing model performance dashboards and addressing any drift alerts. Then, you might collaborate with domain experts to label new data for a compliance classification task. Afternoon could involve coding a new feature pipeline in Scala and conducting experiments with a fine-tuned BERT model on a GPU cluster.

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Interview Prep

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🎯 Who Prewave Is Looking For

  • Proven track record of deploying ML models to production with at least 3 years of experience, specifically in NLP or related fields.
  • Strong hands-on expertise with PyTorch and TensorFlow for model development and fine-tuning.
  • Proficient in Python and Scala for building scalable data pipelines and model serving infrastructure.
  • Solid understanding of SQL and relational databases for data extraction and feature engineering in a supply chain context.

📝 Tips for Applying to Prewave

1

Highlight specific NLP projects you've deployed to production, emphasizing scalability and performance metrics.

2

Demonstrate experience with both PyTorch and TensorFlow; mention any comparative advantages or trade-offs you've handled.

3

Showcase your Python and Scala skills with code samples or links to GitHub repos that include production-ready code.

4

Tailor your resume to include keywords like 'ESG', 'supply chain', 'compliance', and 'sustainability' to align with Prewave's mission.

5

Quantify impact: e.g., 'Improved model accuracy by X%' or 'Reduced inference latency by Y%'.

✉️ What to Emphasize in Your Cover Letter

['Express passion for using AI to drive sustainability and ethical supply chains.', 'Detail your experience with end-to-end ML lifecycle, from data preprocessing to model monitoring.', "Mention specific challenges you've overcome in deploying NLP models at scale.", 'Highlight familiarity with the Austrian tech ecosystem or willingness to relocate.']

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🔍 Research Before Applying

To stand out, make sure you've researched:

  • → Read Prewave's blog and case studies to understand their current NLP applications in supply chain.
  • → Review recent EU regulations on ESG reporting (e.g., CSRD) to grasp the compliance landscape.
  • → Check Prewave's tech stack on their careers page or GitHub if available.
  • → Look into their competitors (e.g., Resilinc, Everstream) to understand their market position.

💬 Prepare for These Interview Topics

Based on this role, you may be asked about:

1 Walk us through an NLP project you deployed to production; how did you handle data quality issues?
2 How would you fine-tune a pre-trained transformer model for a specific supply chain classification task?
3 Describe your approach to monitoring model drift and retraining in a production environment.
4 Explain a situation where you had to choose between PyTorch and TensorFlow; what factors influenced your decision?
5 How do you ensure model fairness and avoid bias in ESG-related predictions?
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⚠️ Common Mistakes to Avoid

  • Submitting a generic resume without tailoring to NLP/supply chain context.
  • Overlooking the importance of Scala; even if you're stronger in Python, acknowledge its relevance.
  • Focusing only on model building without discussing deployment, monitoring, or scalability.

📅 Application Timeline

This position is open until filled. However, we recommend applying as soon as possible as roles at mission-driven organizations tend to fill quickly.

Typical hiring timeline:

1

Application Review

1-2 weeks

2

Initial Screening

Phone call or written assessment

3

Interviews

1-2 rounds, usually virtual

✓

Offer

Congratulations!

Ready to Apply?

Good luck with your application to Prewave!

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