Fraud Analytics Manager, DataVisor

Salary not provided
SQL
Python
Tableau
R
Metabase
Junior and Mid level
Remote in Canada, US

More information about location

DataVisor

ML-powered fraud detection

Open for applications

DataVisor

ML-powered fraud detection

101-200 employees

B2BArtificial IntelligenceSecurityEnterpriseBig dataDeep TechAnalyticsMachine LearningSaaSAPICyber SecurityIdentityFraud

Open for applications

Salary not provided
SQL
Python
Tableau
R
Metabase
Junior and Mid level
Remote in Canada, US

More information about location

101-200 employees

B2BArtificial IntelligenceSecurityEnterpriseBig dataDeep TechAnalyticsMachine LearningSaaSAPICyber SecurityIdentityFraud

Company mission

To deliver the world’s most sophisticated AI-powered solutions to keep companies and their customers safe from fraud and abuse.

Role

Who you are

  • 2+ years of fraud/risk management and analytics experience in banking, fintech, or payment industries, where machine learning models and rules are utilized to prevent scam-based and account takeover-based banking transaction fraud, card payment fraud, etc
  • Excellent data analytics skills using tools like SQL, Python, R
  • Experience in building dashboards through Tableau or Metabase
  • Proven track record performing deep dive analysis on fraud patterns and decision strategies
  • Excellent communication and presentation skills
  • Strong time management skills and a sense of project ownership
  • Prior Banking/FinTech experience is a plus
  • Prior fraud consulting experience is a plus
  • B.A./B.S. degree in a technical or analytical discipline

What the job involves

  • As a Fraud Analytics Manager, (FAM), you are responsible for leveraging our state-of-art fraud detection SaaS products to solve our key clients' fraud and risk problems
  • You provide technical solutions and consultative services of our state-of-art fraud detection SaaS products​ to a portfolio of Fortune 500 customers in FinTech, Banking, E-commerce industries
  • From managing detection rules and models’ performance to providing recommendations on risk strategies and operations, you are an expert who can provide technical guidance, deep dive analysis, and share best practices with customers on using our Machine Learning models, Rules Engine, Device Intelligence Signals, and Case Manager to stay on top of fraud and risks
  • You work cross-functionally with Engineering, ML Modeling, and Product teams to implement new ideas to our analytics solution
  • Understand customers’ risk use cases, challenges, and define plans to achieve success criteria
  • Develop, implement, and evolve risk strategies with clients
  • Perform risk pattern analysis, build Executive dashboards, monitor fraud detection performance
  • Lead technical discussion for fraud deep dives that prepare for detection modules’ success
  • Guide and work with our ML modeling team to monitor and enhance the detection quality by performing UML and/or SML model performance metrics
  • Lead the implementation of detection logic and rules to enhance detection quality
  • Act as technical liaison between clients and internal teams
  • Regularly present customers' key success metrics to internal and external stakeholders
  • This role may require travel to visit customers to strengthen client relationships, onsite workshops, and quarterly business reviews

Our take

As the use of online shopping and banking continues to rise, so too does the opportunity for fraud, with criminals exploiting the gaps in defences that come with an increasingly novel digital environment. DataVisor provides the leading fraud detection solution that leverages machine learning to identify threats, to overcome the security gap caused by the speed at which today’s digital fraudsters evolve.

Traditional fraud solutions have not been designed to handle digital data, and they are especially ill-equipped to deal with quickly changing environments. By employing advanced ML, DataVisor can identify the patterns which allude to fraudulent behaviour, allowing organisations to respond immediately and prevent losses before they happen. Another problem with some systems has been that, in the name of security, they might delay ultimately good customers from accessing services. With DataVisor this becomes a non-issue, as the pattern of a non-malicious user can also be identified with speed.

DataVisor has seen considerable investment - that will fuel its continued expansion across the globe - and has an impressive list of customers. The company has also made several partnerships that will act towards strengthening its product offering. For example, it is extending its analytics and fraud detection through partnership with Equifax, by gaining access to its rich consumer identity data.

Steph headshot

Steph

Company Specialist

Insights

Led by a woman
Top investors

Few candidates hear
back within 2 weeks

Company

Funding (last 2 of 3 rounds)

Dec 2022

$40m

LATE VC

Feb 2018

$40m

SERIES C

Total funding: $94.5m

Company benefits

  • Stock Options
  • Commuter Passes
  • Paid Time Off
  • Catered Lunch and Dinner
  • Team and Company Outings

Company values

  • Take a results-driven approach.
  • Prioritise team spirit and hard work.
  • Be honest, curious, and maintain the highest levels of integrity.

Company HQ

Mountain View, CA

Leadership

Extensive experience in security and AI. Previously a Senior Researcher at Microsoft Research.

Fang Yu

(CPO)

Former Senior Researcher at Microsoft Research, with vast experience in big-data algorithms.

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