Solution Data Scientist

About Us

DataVisor is the world’s leading AI-powered Fraud and Risk Platform that delivers the best overall detection coverage in the industry. With an open SaaS platform that supports easy consolidation and enrichment of any data, DataVisor's fraud and anti-money laundering (AML) solutions scale infinitely and enable organizations to act on fast-evolving fraud and money laundering activities in real time. Its patented unsupervised machine learning technology, advanced device intelligence, powerful decision engine, and investigation tools work together to provide significant performance lift from day one. DataVisor's platform is architected to support multiple use cases across different business units flexibly, dramatically lowering total cost of ownership, compared to legacy point solutions. DataVisor is recognized as an industry leader and has been adopted by many Fortune 500 companies across the globe.

Our award-winning software platform is powered by a team of world-class experts in big data, machine learning, security, and scalable infrastructure. Our culture is open, positive, collaborative, and results-driven. Come join us!

Position Overview

The Solution Data Scientist will be in our Professional Service team. You will be working on various types of technical projects e.g. fraud attack pattern study, risk detection strategy development, risk platform configuration, and machine learning model enhancement. Such projects can be for clients in pre-sales and post-sales stages with DataVisor. You will work closely with Customer Success team, Solutions Consulting team, ML Modeling team, Product team and Delivery team to deliver strong risk detection results and solution configurations that can bring business impacts to our clients and generate revenue for DataVisor. 

Key Responsibilities:

• Business Problem Discovery: Discover and understand the client’s business logics and business problems, e.g. risk decision flows, business pain points, fraud patterns, data availability and quality, and expected business outcome. 
• Data Gathering and Integration: Lead technical discussions with clients’ risk business teams (Fraud and AML) and data team to ensure comprehensive, good quality data is provided. Own the data transfers and data quality checks. 
• Data Processing and Enrichment: Apply your strong data analytical skills to conduct data cleansing, data joining, pattern analysis, and feature engineering, to turn the provided data into enriched datasets.
• Risk Strategy Development: Create features and rules to detect the attack patterns identified; conduct backtest and simulations to evaluate the detection performance;  iterate the strategies until meeting the business requirements. 
• Solution Configurations: configure various DataVisor product components e.g. Data Studio, Feature Platform, Rules Engine, Case Manager, Knowledge Graph, to provide tailored solutions to clients based on their data and business requirements. 
• ML Model Performance Analysis: Work with ML Modeling team to develop, tune, and evaluate fraud detection models (unsupervised and supervised), and conduct performance metrics analysis to guide the threshold selection and decision adoption.
• Project Management and Documentation: Manage the solution service projects with project management tools e.g. Monday.com, and own the creation and reporting of key internal and external-facing project documents as deliverables. 
• Presentation and Demonstration: Present the project results and demonstrate the configured solutions to key business stakeholders.  

• Experience: 
• 2+ years of experience in Fraud and/or AML strategies or models
• 2+ years of experience in client-facing data analytic projects
• Technical Skills: 
• Familiarity with fraud and AML use cases in financial services industry
• Proficient in SQL queries with complex joining and layered logics
• Proficient in Python; familiarity with Java or R
• Proficient in LLM tools e.g. ChatGPT to research new business problems
• Technical product configurations and feature/rule result debugging
• Soft Skills: 
• Excellent communication, presentation, and interpersonal skills
• Strong problem-solving mindset and results-driven mindset
• Strong mindset of documentation and automation with AI tools/agents
• Ability to work collaboratively in a face-paced, team environment and manage multiple projects simultaneously
• Education: 
• Bachelor’s degree in a technical or analytical discipline e.g. Data Science, Statistics, Computer Science