Shift Technology

Data Scientist - Documents

Job description

At Shift Technology, we're transforming insurance with AI. We help insurers fully automate more claims, deliver a great customer experience while protecting against risk and accurately identifying suspected fraud, making internal teams more effective and improving financial performance.
Since our launch in 2014 in Paris, we've raised over $320M with Tier 1 investors, opened offices in Boston, Tokyo, Singapore, London, Madrid, Mexico, Hong-Kong, and Sao Paulo, and currently work with more than 80 of the world's leading insurers. If you are excited about joining a fast-growing insurtech innovator with a passion for excellence and global culture, Shift is the place for you.

Shift Technology offers a unique opportunity to brilliant candidates to accelerate their careers in data science:

  • They work on a broad range of subjects, acquiring a lot of technical and professional experience in ML Engineering, Data Engineering, Data Science, Deep Learning, Software Engineering, Production stabilization and business understanding.
  • Our company is small enough that each person's achievements have an impact on overall performance, yet big enough to be a world leader in our domain.
  • We are a fast growing company, the best contributors will grow to managerial or product tech lead positions much more rapidly than in bigger companies.

YOUR ROLE

The data science team is in charge of building state-of-the-art data processing solutions for Fraud Detection, Automated Adjustment, etc. and of the delivery of Shift products to clients. They focus on the development of the products, to ensure we are always using the best technologies and that our technical platform is competitive, efficient, robust, scalable and easily deployable. More specifically, Data Solution Engineers for Documents will focus on:

  • Training and deploying document classification models;
  • Exploiting OCR models to extract structured information from typewritten documents;
  • Implementing, training and deploying in Production Deep Learning models to automatically detect document falsification;
  • Ensuring the integration of Deep Learning models within our software, their deployment in Production on dozens of customer environments, and having them run at scale with large volumes of documents. Learning about robustness patterns, code optimization techniques and large-scale deployment organization.

WHAT WE ARE LOOKING FOR

  • Previous experience on Document Processing (min. 3 years); a focus on the automatic detection of document falsification methods would be a plus.
  • Deep Learning background with especially good understanding of underlying theoretical concepts as well as practical experience (model training, architecture, optimization, etc.)
  • Good programming skills, such as university courses focused on computer science and/or some coding work experience and you feel comfortable spending time writing and understanding code.
  • Ability to produce optimized and stable code which will smoothly run in a production context; you're familiar with algorithm complexity assessment; you can write unit tests and integration tests; you think about potential problems and edge cases.
  • Good practical spirit, eager to understand how new systems work, and able to troubleshoot and improve them; when hitting an unexpected problem, you are ready to dig in to solve the problem yourself.
  • Teamwork capabilities; you are comfortable working with others and you have good communication skills.
  • English speakers; we are an international company with offices in many countries and 40+ nationalities, the Shift working language is English.

EEO Statement

At Shift we thrive to be a diverse and inclusive workforce. We hire and trust people without regard to race, color, religion, marital status, age, national or ethnic origin, physical or mental disability, medical condition, pregnancy, genetic information, gender identity or expression, sexual orientation, or other non-merit criteria. Shift is proud to be an Equal Opportunity Employer.

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