Staff Data Scientist - MoneyLion

About Gen:

Gen is a global company dedicated to powering Digital Freedom through its trusted consumer brands including Norton, Avast, LifeLock, MoneyLion and more. Our combined heritage is rooted in financial empowerment and cyber safety for the first digital generations, and today we deliver award-winning cybersecurity, online privacy, identity protection and financial wellness solutions to nearly 500 million users in more than 150 countries.

Together, we share a collective passion and vision to protect consumers and help them grow, manage and secure their digital and financial lives. We’re always looking for smart, fearless and high-impact talent who see AI as a teammate – leveraging it to move faster and deliver meaningful results.

When you’re part of Gen, you’ll have the flexibility, tools and support to do your best work and grow your career – from flexible working options and time off to competitive pay, benefits and well-being programs.

At Gen, we are scrappy and relentlessly customer driven. We create room for healthy debate, experimentation and continuous learning, and we seek out people with different experiences, identities and ideas to join our team. You’ll work with people who back each other, respect each other and understand that our differences are a competitive advantage.

If this sounds like you, we’d love you to be part of Gen.

About the Role:

We are seeking a Staff Data Scientist to join the Data Science team at Engine by MoneyLion. Engine by MoneyLion is the definitive search engine and marketplace for financial products, connecting consumers with personalized financial offers across loans, deposits, credit cards, and more through its robust API. Data Science powers Engine’s offer recommendations, dynamic pricing, and enhanced decisioning across our network, working closely with financial partner managers and product teams to develop cutting-edge models that drive value across the consumer journey through our complex marketplace funnels.

In this role, you will own critical model systems end-to-end—from data engineering and feature pipeline management through model development, deployment, and real-time serving. Your models will generate direct revenue impact in real time, and your data solutions will affect millions of users daily. You will lead technical initiatives across recommendations, pricing, and marketplace optimization, collaborating deeply with engineering, product, and business stakeholders to bridge the gap between advanced machine learning and tangible business outcomes.

Key Responsibilities:

• Own and develop production ML models for real-time recommendations, pricing, and conversion prediction across the Engine marketplace.
• Design, build, and manage feature pipelines and data transformations in our data warehouse (e.g., Redshift, Snowflake) using tools like dbt, Airflow, and SQL to ensure high-quality, timely features for model training and serving.
• Lead the design, execution, and analysis of large-scale A/B tests and experiments, translating results into actionable product and model improvements.
• Collaborate closely with product managers, partner managers, and business stakeholders to translate complex business problems into well-scoped data science projects.
• Work hand-in-hand with engineering teams to deploy, monitor, and maintain ML models in production—including real-time serving infrastructure.
• Drive best practices across the team in model development, code quality, documentation, experiment design, and reproducibility.
• Contribute to the evolution of our MLOps platform and tooling, ensuring scalable and reliable model lifecycle management.
• Present findings, model insights, and strategic recommendations to executive and non-technical stakeholders with clarity and business context

About You:

• Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, Physics, Economics, or a related quantitative field (or equivalent professional experience).
• 7+ years of experience across data science, machine learning, and data engineering, including:
• Designing and shipping production ML models and advanced analytics in applied,

production-oriented settings using Python, SQL, and ML frameworks.

• Building real-time or near-real-time ML systems for recommendations, pricing, bidding, or similar use cases.
• Working with data warehouse technologies (Redshift, Snowflake, BigQuery) and building/managing data pipelines (dbt, Airflow, Spark).
• Strong foundation in statistics, probability, experiment design, and machine learning theory.
• Experience working with ML platforms and infrastructure (SageMaker, Spark, Ray,

MLflow, or equivalent).

• Comfortable doing software engineering when needed—writing application code in Python/Scala/Java, contributing to APIs, containerizing services (Docker, Kubernetes), or building CI/CD for model deployments.
• Excellent communication skills—effective with both technical and non-technical audiences.
• Experience in fintech, financial services, or marketplace/auction environments is a strong plus.

What’s Next:

• Recruiter Interview
• Hiring Manager Interview
• Technical Interview
• Final Interview