Data Scientist

Website thomsonreuters Thomson Reuters

the answer company

Job Description

Successful applicant will join TR Labs – Boston as a data scientist. The Lab is one of five Labs globally that find new ways to leverage Thomson Reuters enormous data holdings and the latest big data/AI/Machine (deep) Learning technologies to develop new solutions for our customers, both internal and external. We are tackling very important issues in natural language processing and data analytics for the company across all business unites. The Labs also partner with universities and startups to help Thomson Reuters understand the art of the possible. Work in the Labs is fast paced, and scientists are expected to be self-motivated and self-directing but also to respond well to collegial feedback and coaching. The Labs are part of an effort to enable new technologies and technology skills across the organization, so excellent communication skills are necessary.

Job Description

  • Collaborate with dozens of data scientists, machine learning scientists and deep learning specialists—PhD’s and Masters grads with experience in deep learning, machine learning, big data, statistics, natural language processing, probabilistic programming and data visualization—at Thomson Reuters Labs around the world to build AI systems that push the boundaries of what was previously considered possible.
  • Continually research and learn how to apply state-of-the-art deep learning NLP techniques to keep your skills on the cutting edge of what is possible.
  • Interact with customers and internal business partners to understand their business challenges and develop hypotheses about AI solutions to real world problems in legal, regulatory, news and tax domains.
  • Run experiments with the latest deep learning models using frameworks such as TensorFlow and PyTorch, and toolkits such as Tensor2Tensor, Sockeye, and OpenNMT.
  • Develop new deep learning models and innovative machine learning pipelines to solve real world business problems.
  • Work with engineering teams to move your solutions into production.

Basic Qualifications

  • 2+ years of relevant experience in building machine learning models and/or systems
  • Ability to translate the latest technical papers into implementations addressing our use cases.
  • Development skills to rapidly deliver minimum viable products.
  • Strong experience with scripting languages like Python or R.

At Thomson Reuters, we believe what we do matters. We are passionate about our work, inspired by the impact it has on our business and our customers. As a team, we believe in winning as one – collaborating to reach shared goals, and developing through challenging and meaningful experiences. With more than 25,000 employees in more than 100 countries, we work flexibly across boundaries and realize innovations that help shape industries around the world. Making this happen is a dynamic, evolving process, and we count on each employee to be a catalyst in driving our performance – and their own.

As a global business, we rely on diversity of culture and thought to deliver on our goals. To ensure we can do that, we seek talented, qualified employees in all our operations around the world regardless of race, color, sex/gender, including pregnancy, gender identity and expression, national origin, religion, sexual orientation, disability, age, marital status, citizen status, veteran status, or any other protected classification under applicable law. Thomson Reuters is proud to be an Equal Employment Opportunity/Affirmative Action Employer providing a drug-free workplace.

We also make reasonable accommodations for qualified individuals with disabilities and for sincerely held religious beliefs in accordance with applicable law.

Intrigued by a challenge as large and fascinating as the world itself? Come join us.

To learn more about what we offer, please visit thomsonreuters.com/careers.

More information about Thomson Reuters can be found on thomsonreuters.com.

To apply for this job please visit jobs.thomsonreuters.com.

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