Senior Data Scientist - Discovery

Overview

At Branch, we power every touchpoint with links that work and insights that prove it. From click to conversion, we make growth measurable. Our attribution, backed by AI-enhanced linking, is trusted to deliver seamless experiences that increase ROI, decrease wasted spend, and eliminate siloed attribution.

We bring the same rigor to how we build our team, by empowering our people to move fast, own outcomes, and build something that matters. We take pride in making meaningful investments in our team’s health, wealth, and growth so individuals can thrive as we scale. Our culture values smart, humble, and collaborative teammates who take accountability and drive results in an environment where their work truly moves the business forward.

We are innovative, scaling with purpose, and led by seasoned leaders who know how to build enduring companies. Trusted by brands like Instacart, Western Union, NBCUniversal, Zoc Doc, and Sephora, we’re big enough to matter, small enough for you to make a real impact. If you’re excited by the grit of building, rapid learning, and shaping the future of customer growth, you’ll find your place here.

The Discovery team is at the forefront of driving Branch’s success in the mobile growth and advertising ecosystem. Our mission is to intelligently connect the right ads to the right users at the optimal moment, maximizing brand awareness and return on ad spend (ROAS) for our customers while ensuring a high-quality user experience.

• Lead the design, development, and deployment of high-impact machine learning models (e.g., predicted click-through rate, conversion rate, bidding optimization, and lookalike targeting) directly into our production environment.
• Drive complex, ambiguous, and high-scale ML projects from initial research and proof-of-concept through to large-scale deployment, working closely with software engineering teams.
• Design and execute rigorous online A/B experiments to test hypotheses, measure model performance, and statistically analyze results to drive continuous product improvement.
• Perform hands-on analysis and modeling of enormous, complex data sets to identify predictive signals and develop new features that enhance targeting and sourcing efficacy.
• Establish scalable, efficient, and automated processes for large-scale data analysis, machine learning model development, validation, and serving.
• Stay abreast of the latest research in ML, deep learning, optimization, and Ad Tech, and apply new, innovative approaches to Branch’s unique challenges in ad delivery and optimization.
• Communicate complex findings and product vision to engineers, product managers, and business stakeholders, influencing the roadmap for the Discovery platform.

• 4+ years of professional experience building and deploying machine learning models for business applications.
• Strong programming proficiency in one or more relevant languages (e.g., Python, Java, C++).
• Deep expertise in foundational computer science areas, including algorithms and data structures, numerical optimization, data mining, and distributed computing (e.g., Spark, Hadoop).
• Experience with ML frameworks and libraries (e.g., PyTorch, Tensor Flow, Scikit-learn).
• Proven ability to translate business problems into mathematical and ML frameworks.

• Experience in professional software development practices, including system design, code review, and production environment management.
• Deep familiarity with the Ad Tech ecosystem (e.g., DSPs, SSPs, Exchanges, Advertiser/Publisher platforms) and related challenges like auction dynamics, bidding strategies, and anti-fraud systems.
• Experience working with massive-scale data (billions of daily events) and petabyte-scale data processing systems.
• Prior experience developing models specifically for recommender systems, personalization, or large-scale optimization problems.
• Familiarity with cloud computing environments (AWS, GCP, or Azure).

This role does not qualify for relocation or visa sponsorship. This role is 100% remote in Denver. Other locations in the US may be eligible as well.

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