Data Scientist - NEAR : Job Details

At OKX, we believe that the future will be reshaped by crypto, and ultimately contribute to every individual's freedom. OKX is a leading crypto exchange, and the developer of OKX Wallet, giving millions access to crypto trading and decentralized crypto applications (dApps). OKX is also a trusted brand by hundreds of large institutions seeking access to crypto markets. We are safe and reliable, backed by our Proof of Reserves. Across our multiple offices globally, we are united by our core principles: We Before Me, Do the Right Thing, and Get Things Done. These shared values drive our culture, shape our processes, and foster a friendly, rewarding, and diverse environment for every OK-er.

The Risk Engineering org at OKX is responsible for the overall risk and fraud prevention at the company. We're a team of engineers who work closely with business teams on payment risk, fraud risk, identity & KYC risk, p2p risk, growth risk, OKX pay risk, OKX card risk, content risk, and several other risk domains. We're also deeply committed to moving the needle on risk standards for the crypto industry, building products in a compliant way, and protecting the company from financial, regulatory and reputational risks. Our team consists of SWEs and MLEs who are based across our global offices.

We're excited to welcome our first Data Scientist to the team where you'll be bringing a strategic mindset, strong analytical skills, and a collaborative spirit. We're looking for someone with deep intellectual curiosity and a passion for turning data into meaningful business outcomes.

• Analyze the whole user journey and identify complex fraud patterns, especially individual and group patterns through detailed data mining and analysis.
• Link Analysis/Graph analytics to find and mitigate deeply-connected fraud networks and detect new accounts being added to these networks
• Collaborate across technology and business stakeholders to communicate analytical findings to both technical and non-technical audiences.
• Provide technical guidance for engineering projects that incorporate new data points into the investigation team's toolkit, such as API integrations or internal data transformations.

• Minimum of 6 years of professional experience in Data Science.
• Master's degree (or PhD) in Statistics, Mathematics, Computer Science, Engineering or other quantitative discipline.
• Successful track record of owning and driving large, complex data analysis projects.
• Demonstrated capacity for innovation and outside-the-box thinking in the creation of new capabilities and processes that are unstructured or exploratory in nature.
• Move-fast mindset and able to drive materialized impact

• Experience in crypto and blockchain technologies
• Background in fraud analytics within financial services or FinTech environments
• Willingness and ability to travel as required for the role
• Strong communication skills in both written and spoken Mandarin are highly preferred

• Competitive total compensation package
• L&D programs and Education subsidy for employees' growth and development
• Various team building programs and company events

The salary range for this position is $214,666 to $375,666. The salary offered depends on a variety of factors, including job-related knowledge, skills, experience, and market location. In addition to the salary, a performance bonus and long-term incentives may be provided as part of the compensation package, as well as a full range of medical, financial, and/or other benefits, dependent on the position offered. Applicants should apply via OKX internal or external careers site.

OKX is committed to equal employment opportunities regardless of race, color, genetic information, creed, religion, sex, sexual orientation, gender identity, lawful alien status, national origin, age, marital status, and non-job related physical or mental disability, or protected veteran status. Pursuant to the San Francisco Fair Chance Ordinance, we will consider employment-qualified applicants with arrest and conviction records.