Data Science Expertise in AI Solutions

Vor 6 Tagen

Zürich, Zürich, Schweiz Artifact SA Vollzeit

About Artifact SA

We empower people with AI for the better at Artifact SA. As a professional service provider, our expertise lies in Data Science. Together, we leverage the best ideas to find pragmatic and sophisticated solutions with the best-suited technology.

Our mission is to help our business clients accelerate their journey with AI and deliver real impact. We are driven by innovation in our minds, people in our hearts, and agility in our veins.

Your Responsibilities

  • Transform our clients' data into tangible business outcomes
  • Design, implement, and deploy AI solutions using open-source technology to solve pressing business problems
  • Work in high-performing, agile delivery teams and take accountability across the full project life cycle, including quality, solution design feasibility, and business development activities
  • Communicate actionable and meaningful outcomes and contribute to the development of new solutions, assets, and products
  • Built trusted relationships with various stakeholders across our clients, delivery teams, and partners

What We Offer

  • A dynamic environment where you can grow, contribute, and shape your ideas
  • A chance to work with great talents who want to change the future with AI
  • A flexible working model that suits your needs
  • The opportunity to further shape the start-up and contribute to its fast growth
  • A collaborative team and lots of fun while delivering fascinating projects with well-known Swiss clients

Skills and Qualifications

  • First-hand experience in Data Science work with a deep understanding of Machine Learning, Deep Learning, Natural Language Processing, and Computer Vision
  • Fluency in Python and additional languages such as R, SQL, or others
  • Familiarity with analytics libraries, visualization tools, and Big Data frameworks like NumPy, SciPy, Pandas, scikit-learn, PyTorch, TensorFlow, Hive, and Spark
  • Experience in architecting and bringing analytics solutions into production and scaling them afterwards based on Azure, AWS, and GCP with DevOps, MLOps, and DataOps

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