Launch Program: Associate Data Science Product Manager

Overview

Launch Program Overview:

Skip the “entry-level grunt-work” and start making a real impact on Day 1. The Launch Program is your opportunity to onboard into your full-time role at Red Ventures in a cohort-style environment that provides in-depth training on our beliefs, culture, business model, and leadership. Your first few weeks will be spent alongside a cross-functional cohort of new Red Ventures employees starting their careers across various roles and teams.

These individuals will become your built-in support system and community as you navigate the start of your career with us. After your initial onboarding period, you’ll join one of our teams, meet your manager and mentor, and find unparalleled opportunities to make a substantial impact and quickly advance your career.

We believe successful Data Science Product Managers have a wide set of experiences and skill sets in the data domain. This hybrid position offers you hands-on opportunities in data science, data analysis, and product management. In your first two years, you’ll get repetitions in contributing to product life cycles via product analytics, roadmap support, model building, and influential communication with product leadership.

What You ll Do:

As an Associate Data Science Product Manager, you will play a pivotal role in bridging the gap between complex data science initiatives and impactful product solutions to create value for both our business and our customers.

• Responsible for identifying opportunities to increase the impact of your product through deep understanding of the problem space, thorough comprehension of the product strategy and technology, and advanced statistical analysis. Effectively communicating recommendations to your product leads to create buy-in and action.
• Translate your recommendations into clear work streams and features that contribute to the product roadmap. Collaborate with Data Science, Design, and Engineering teams to turn objectives into detailed requirements with assistance from your product lead.
• Leverage algorithms and programming tools to execute relevant technical work streams. This includes building items such as:
• Data pipelines to gather, manipulate, prepare, and visualize large datasets
• Machine learning models for offline and proof-of-concept use cases
• Agentic workflows to automate tasks or for reasoning capabilities for specific features in your product
• End to end ownership of experiments. Assist in deploying features aimed at improving core KPIs. Conduct analyses to assess the impact of the experiments, synthesizing and owning the narrative.
• Promote adoption and usage of your product and tooling. Interact directly with end-users for training, feedback, and user testing.
• Stay up-to-date with advancements in machine learning and generative AI to identify innovative ways to solve business challenges.

Who you are:

• Strong analytical and critical thinking skills. Can connect the dots between the business problem, the approach, the data, and the relevant recommendation.
• Strong grit and learning mentality when faced with challenging projects, unfamiliar technology, and ambiguous business problems. Takes a proactive approach to overcoming obstacles.
• Creative problem solver with an entrepreneurial mindset and a strong business acumen with an interest in solving business and customer problems to create impact.
• Showcases high EQ - can collaborate effectively in a team environment.

Experiences you’ve had:

• Graduating with a bachelor s or master s degree in Summer 2026, with academic and/or practical experience in fields such as computer science, data science, statistics, or product management. (Start date in our August 2026 cohort)
• Demonstrated ability to collect, mine, and manipulate large data from disparate data sources, with an ability to dig deep and understand the process (e.g., SQL, Pandas, Tableau).
• Hands-on application of machine learning and/or generative AI to a relevant problem through statistical programming languages or large language models (e.g., Python, R, GPT). Thorough knowledge in statistics and machine learning techniques.
• Demonstrated ability to root cause problems through a data-driven…