Data Scientist III

Remitly•Minneapolis, MN

1d•Hybrid

About The Position

LexisNexis Risk Solutions is the essential partner in the assessment of risk. Within our Business Services vertical, we offer a multitude of solutions focused on helping businesses of all sizes drive higher revenue growth, maximize operational efficiencies, and improve customer experience. Our solutions help our customers solve difficult problems in the areas of Anti-Money Laundering/Counter Terrorist Financing, Identity Authentication & Verification, Fraud and Credit Risk mitigation and Customer Data Management. This role is an opportunity to join a vibrant, collaborative team as a data scientist conducting statistical analysis and building predictive models for a variety of performance outcomes such as fraud and credit risk for one or more industries including (but not limited to) consumer and small business lending, telecommunication, retail, and e-commerce. You would be expected to have a firm understanding of data mining, statistical methods and multiple modeling techniques. As part of the team you would be tasked with finding innovative ways to produce solutions to serve our customers and to continually expand your expertise.

Requirements

Bachelor’s degree in statistics, data science, mathematics or quantitative methods and at least four years of relevant work experience.
At least two years of experience building predictive models using machine learning and conducting data analysis using R, Python or similar software packages.
At least two years of experience with mainstream programming languages, such as Java, R, Python, or Scala, in a collaborative environment using software development best practices such as version control and testing.
Superior knowledge of data science frameworks, statistical methods and advanced machine learning techniques.
High degree of creative, analytical and problem-solving skills.
Ability to learn quickly.
Ability to work effectively both independently and collaboratively.
Ability to communicate complex technical or statistical concepts to a non-technical audience.
Ability to apply modern data exploration and visualization techniques to deliver actionable insights.
Willingness to adapt to new techniques and an innovative attitude towards finding solutions.
Fluency with presentation and document programs such as PowerPoint, Word, Excel.

Nice To Haves

Master’s degree in statistics, data science, mathematics or quantitative methods and at least two years of relevant work experience.
Experience with big data technologies and applying large scale machine learning techniques.
Experience with version control through GitHub.
Experience with Unix/Linux system architecture and command line tools.
Experience in credit or fraud risk management industry.

Responsibilities

Understand and execute analytic plans with the appropriate statistical or modeling technique.
Conduct analyses supporting existing and new product development or customer sales opportunities.
Assemble, merge and parse large amounts of data to detect meaningful trends and patterns.
Explore opportunities to enhance existing products with new features or new data sources.
Develop machine learning models, create model code, and work with internal or external stakeholders to validate accuracy of production code.
Interpret, document, and communicate analytic work to non-technical audiences.
Proactively identify and communicate data quality issues and successfully work with other teams to implement solutions.
Collaborate with cross-functional peers to define product and data science strategies.
Provide support to other data scientists across the organization.
Critical reviews of data experiments to ensure accuracy, completeness, and feasibility.
Other duties as assigned.