Manager, Data Scientist
Financial Services
Manager,DataScientist
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“Manager, Data Scientist at Manager, Data Scientist. Skills: Machine learning, Fraud prevention, Data science, Model deployment. Build and deploy machine learning models. Optimize models for segments”
What You'll Achieve.
Secure credit card portfolio; Provide financial protection; Achieve business value; Improve fraud capture rates; Enhance customer safety
Industry & Context.
Data-driven decision making
What They're Looking For.
Must Have
Bachelor's Degree in quantitative field plus 6 years of experience, Master's Degree in quantitative field or MBA with quantitative concentration plus 4 years of experience, PhD in quantitative field plus 1 year of experience, 1 year experience leveraging open source programming languages, 1 year experience working with machine learning, 1 year experience utilizing relational databases
Nice to Have
PhD in STEM field plus 3 years of experience, 1 year experience working with AWS, 4 years experience in Python for large scale data analysis, 4 years experience with machine learning model development, 4 years experience with SQL, Experience with big data and distributed computing, Experience with Spark, Experience with model risk governance, Experience technically leading and developing a team, Experience with traditional machine learning, Experience with emerging GenAI techniques
What You'll Do.
Build and deploy machine learning models
Optimize models for segments
Improve fraud capture rates
Enhance customer safety
Detect and mitigate first-party fraud
Deliver production-ready insights
Partner with cross-functional team
Leverage technologies to reveal insights
Build machine learning models through all phases
Monitor and support continuous model deployment
Collaborate on design and maintenance of solutions
Write clear technical documentation
Ensure models adhere to best practices
Maintain regulatory compliance
Maintain model inventory records
Execute model testing and change control protocols
Collaborate on independent model validation
Collaborate on compliance risk assessments
Translate complexity into business goals
How You'll Work.
Team & Collaboration
Cross-functional team; Data scientists; Software engineers; Product managers
Communication Scope
Technical documentation; Translate complexity
Full Job Description
Manager, Data Scientist - Card Payment Fraud Prevention Data is at the center of everything we do. As a startup, we disrupted the credit card industry by individually personalizing every credit card offer using statistical modeling and the relational database, cutting edge technology in 1988! Fast-forward a few years, and this little innovation and our passion for data has skyrocketed us to a Fortune 200 company and a leader in the world of data-driven decision-making. As a Data Scientist on the Card Payment Fraud Prevention team, you'll lead the charge against first-party fraud. You will build and deploy mission-critical machine learning models that operate across billions of transactions to secure the entire credit card portfolio. You will research, build, and deploy advanced machine learning solutions using a cutting-edge tech stack. Your work will directly translate to massive financial protection and business value from reduced credit losses. The mission includes optimizing models for highly challenging and expanding segments to improve fraud capture rates and enhance customer safety. _****Team Description****_ The Card Payment Fraud Prevention data science team detects and mitigates first-party fraud by building and deploying machine learning models that keep customer accounts safe and compliant. Leveraging big data and a modern tech stack—including Python, Spark, Ray, H2O, PyTorch, and Kubernetes—the team delivers production-ready insights with a focus on both speed and sustainable impact, combining deep experience in traditional ML with an appetite for AI-based development. _****In this role, you will:****_ * Partner with a cross-functional team of data scientists, software engineers, and product managers to deliver a product customers love * Leverage a broad stack of technologies — Python, Conda, AWS, H2O, Spark, and more — to reveal the insights hidden within huge volumes of numeric and textual data * Build machine learning models through all phases of dev
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