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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.

As a Senior Data Scientist on this team, you will be instrumental in building the next generation of our ad delivery platform. You will develop, productionize, and deploy sophisticated machine learning models that are essential for optimizing ad delivery performance through novel sourcing and targeting techniques. This role requires a blend of rigorous scientific research, engineering execution, and deep understanding of the Ad Tech domain.

Key Responsibilities

• 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.

Basic Qualifications

• 4+ years of professional experience building and deploying machine learning models for business applications.
• PhD in Computer Science, Engineering, Machine Learning, Statistics, or a related quantitative field, OR a Master's degree in a related field and 4+ years of relevant experience.
• 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, TensorFlow, Scikit-learn).
• Proven ability to translate business problems into mathematical and ML frameworks.

Preferred Qualifications

• 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 Canada. This role is not eligible for remote work in any other location.

In accordance with applicable law, the following represents a reasonable estimated compensation range for this role: the estimated pay range for this role, if based in Canada is 148,000 CAD to 189,000 CAD. Please note that this information is provided for those hired in Canada only. Compensation for candidates outside of Canada will be based on the candidate’s specific work location. Actual compensation will be determined based on skills, experience, and geographic location and may be more or less than the amount shown above.