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Data Scientist



Data Science
United States · Remote
Posted on Tuesday, April 30, 2024

Verusen is a leading technology company that uses artificial intelligence to provide visibility, digitization and prediction of materials data and inventory for complex supply chains. The company’s AI software harmonizes disparate material data across ERP instances/systems while providing accurate MDM across the enterprise to optimize inventory costs. Intelligent controls enforce inventory procedures to help prevent future inventory spikes, while predictive capabilities optimize allocation and procurement needs. The result is a data foundation you can trust to move quickly to innovate and support related Industry 4.0 initiatives.

Verusen is venture-backed by leading investors from San Francisco to Boston, and is a Signature Company at Georgia Tech’s Advanced Technology Development Center (ATDC). Partnerships including SAP and Accenture. Verusen is a portfolio company of SAP.iO.

Role Overview:

As a Data Science Engineer at Verusen, you will have the opportunity to stay on the cutting edge of research and put this research to practical use by building robust software and infrastructure systems. You will blend the responsibilities of a traditional data scientist with those of a backend engineer, ensuring that the models you develop are seamlessly integrated into our production environment.


  • Solve complex problems by breaking them down and crafting innovative solutions.
  • Partner closely with cross-functional teams to identify areas of opportunity and drive results.
  • Execute strategic initiatives from start to finish, including data collection, modeling, and the communication of insights and recommendations.
  • Develop and deploy end-to-end ML/AI systems, ensuring they are scalable and maintainable.
  • Write well-structured, efficient, and maintainable code to turn models into production-ready applications.
  • Maintain and optimize existing machine learning infrastructure and pipelines.
  • Collaborate with backend engineers to integrate ML models with other software systems.

Skills and Qualifications:

  • 3-5 years of experience developing and deploying end-to-end ML/AI systems.
  • Practical experience working with structured and unstructured data, data processing at scale, feature engineering, and model operationalization.
  • Expertise with TensorFlow, PyTorch, Scikit-learn, Pandas, and NumPy.
  • Experience with large language models (LLMs), understands vectors, embeddings, "x"-shots, etc.
  • Proficient with Python, including the scientific Python stack and machine learning frameworks.
  • Strong theoretical foundation in statistics, including various common distributions and probability theory.
  • Good knowledge of modern machine learning software and libraries.
  • Data engineering skills: proficient in SQL using PostgreSQL and Snowflake; experience with Spark and Hadoop.
  • Experience in backend engineering: proficient in software engineering principles, version control systems (e.g., Git), and RESTful APIs.
  • Advanced degree in Statistics, Analytics, Engineering, Computer Science, or another relevant engineering field is a plus.

Bonus Points:

  • Experience in fast-paced, startup environments.
  • Understanding of what it takes for a team to win in competitive environments and relishes the challenge.

Commitment to Diversity and Inclusion

At Verusen, we are committed to providing an environment of mutual respect where equal employment opportunities are available to all applicants and teammates without regard to race, color, religion, sex, national origin, age, physical and mental disability, sexual orientation, gender identity and/or expression, status as a veteran and any other characteristic protected by applicable law. We respect and seek to empower each individual and support a diverse culture, perspectives, skills, and experiences within our workforce. We believe that diversity and inclusion among our teammates are critical to our success, and we seek to recruit, develop and retain the most talented people from a diverse candidate pool.