Data Scientist

About the role:

The Data Scientist will be responsible for managing, understanding and analyzing in-house and customer data – including text mining, developing predictive systems, risk scoring, creating efficient algorithms, data quality improvement and other related activities. This individual will work closely with the TRSS Analysts to drive, identify, evaluate, design and implement statistical analyses of gathered open source, proprietary, and customer data to create analytic metrics and tools to support TRSS analysts, customers and existing product offerings.

Successful candidates will have the opportunity to contribute directly to the features and capabilities deployed in our applications. They will work with customers to assist in gathering requirements and contributing to Statements of Work (SOWs) for new sales or POCs and executing design post sale while getting deeply involved into the delivery of the proposed solutions. The role will interface with the customer and provide continuity of technical and data-exploration expertise to ensure we are delivering a workable solution that meets the customer requirements and technical capabilities. The position requires a proactive, mission-oriented person who strives to produce the best possible work for the customer.

Job Description

Define, manipulate, aggregate and use both structured and unstructured “big data” in order to support
descriptive and predictive analytics across the businesses.

• Collaborate with scientists, product groups and content groups to perform “big data” aggregations, symbology mapping, and manipulations of important data-sets
• Perform statistical (and machine learned) analyses on data to serve business purposes
• Narrate stories (sometimes to a non-technical audience) about our content and processes by data analysis and visualization
• Define and develop software for the analysis and manipulation of large and very large data-sets
• Guide the architecture of “big-data” business processes with an eye towards robustness, parsimony and reproducibility (at senior levels)

Additional Information

Are you passionate about the chance to bring your data quality improvement experience to a world class organization that is leading the way in both content and technology to serve and protect our citizens home and abroad? Do you have the skills necessary to manage, understand, and analyze inhouse
and customer data including text mining, developing predictive systems, risk scoring, creating efficient algorithms, data quality improvement and other related activities? Then Thomson Reuters Special Services (TRSS) is looking for you!

What You’ll Do: As a Data Scientist, you will be responsible for driving, identify, evaluate, design and implement statistical analyses of gathered open source, proprietary, and customer data to create analytic metrics and tools to support TRSS analysts, customers and existing product offerings. Successful
candidates will have the opportunity to contribute directly to the features and capabilities deployed in our applications. They will work with customers to assist in gathering requirements and contributing to Statements of Work (SOWs) for new sales or POCs, and executing design post sale while getting deeply
involved into the delivery of the proposed solutions. The role will interface with the customer and provide continuity of technical and data-exploration expertise to ensure we are delivering a workable solution that meets the customer requirements and technical capabilities. The position requires a proactive, mission-oriented person who strives to produce the best possible work for the customer.

As the Data Scientist, you will also contribute to a variety of areas including:

• Working with interdisciplinary engineering and research teams on designing, building and deploying data analysis systems for large data sets.
• Working closely with customers to apply data science to their mission specific content.
• Creating algorithms to extract information from large data sets.
• Establishment of scalable, efficient, automated processes for model development, model validation, model implementation, and large-scale data analysis.
• Development of metrics and prototypes that can be used to drive business decisions.
• Providing thought-leadership and dependable execution on diverse projects.
• Identification of emergent trends and opportunities for future client growth and development.
• Researching and identifying Artificial Intelligence (AI) methods – including Machine Learning (ML) and Natural Language Processing (NLP) methods.
• Identification of new applications of AI in the conte