Intern, Data Science

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. 

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. 

How Will You Make An Impact?  

Are you ready to make an impact? If you’re looking for an internship that gives you hands-on experience in a fast-paced, high-impact technology environment, especially one at the forefront of cybersecurity—you’re in the right place. 

From product innovation to data science, marketing, engineering, and beyond—you’ll work on real projects, solve real problems, and contribute to meaningful work that supports our mission. 

What You’ll Do 

• Experiment Test Plan Organization 

• Help structure and document a standardized experiment analysis plan. 

• Create repeatable documentation templates for future HTE analysis. 

• Double ML Tooling Leverage & Refinement 

• Assist in refining feature inputs and model specifications. 

• Support validation of assumptions and model stability. 

• Ensure reproducibility and documentation of DML methodology. 

• Heterogeneous Treatment Effect Analysis (Applied Test) 

• Select and analyze one live or recent experiment. 

• Use DML to estimate heterogeneous treatment effects across: 

• Risk tiers 

• Score bands 

• Traffic cohorts 

• External signal overlays (if applicable) 

• Compare heterogeneous results vs average treatment effect. 

• Findings & Business Recommendations 

• Translate technical DML outputs into actionable Yield recommendations. 

• Identify segments with: 

• Positive incremental lift 

• Neutral impact 

• Negative or adverse effects 

• Recommend target