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Sr Data Scientist at PayPal

Toronto, Ontario, Canada -

Full Time

Start Date

Immediate

Expiry Date

29 Jan, 26

Salary

0.0

Posted On

31 Oct, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Science, Python, SQL, Analytical Thinking, Problem Solving, Data Quality, Mentoring, Collaboration, Fraud Risk, Stakeholder Engagement, Business Judgment, Strategic Thinking, Communication, Detail Orientation, Flexibility, Project Management

Industry

Software Development

Description

Lead the development and implementation of advanced data science models. Collaborate with stakeholders to understand requirements. Drive best practices in data science. Ensure data quality and integrity in all processes. Mentor and guide junior data scientists. Stay updated with the latest trends in data science. Work closely with partners in Risk Platform, Data Science, Operations, Product Management, Legal & Compliance and other teams to formulate and execute fraud risk solutions Minimum of 5 years of relevant work experience and a Bachelor's degree or equivalent experience. Bachelor's degree or higher in Mathematics, Statistics, Operations Research, Finance, Economics or related quantitative discipline 5+ years proven credit or fraud risk analytics experience or equivalent Must be an intuitive, organized analytical thinker, exceptional problem solver with the ability to perform detailed analysis Strong written, oral, and interpersonal skills a must including the ability to explain and/or present analysis Aptitude and willingness to roll-up the sleeves and get involved in the details Must have good business judgment with demonstrated ability to think creatively and strategically Ability to work with leadership & stakeholders to define project scope and direction, driving large pieces of the work independently Ability to maintain a flexible work schedule to collaborate effectively with teams across multiple time zones Proficient in Python, SQL

Responsibilities

Lead the development and implementation of advanced data science models while collaborating with stakeholders to understand requirements. Drive best practices in data science and ensure data quality and integrity in all processes.