Full-Time Staff Data Scientist - Ad Display

At Branch, we’re transforming how brands and users interact across digital platforms. Our mobile marketing and deep linking solutions deliver seamless experiences that increase ROI, decrease wasted spend, and eliminate siloed attribution. We seek an experienced Data Scientist to optimize ad display on native Android surfaces.

Responsibilities:

• Develop data-driven ad optimization strategies for native Android.
• Design, implement, and improve predictive models for ad targeting, recommendation, and bidding.
• Lead the design and analysis of experiments to test ad placements, formats, and targeting.
• Analyze user behavior to optimize ad positioning, timing, and relevance.
• Partner with product managers, engineers, and marketing teams to align ad strategies.
• Communicate insights and recommendations to technical and non-technical stakeholders.
• Ensure scalability of ad models across devices and networks.
• Deploy and monitor models in production.
• Mentorship of junior data scientists.

Requirements:

• PhD (preferred) or Master's Degree (required) in Computer Science, Data Science, Statistics, Machine Learning, or related field.
• 8+ years of experience in data science or machine learning, with a focus on ad technology or user experience optimization.
• Experience with ad serving, ad tech stacks, and optimization techniques (Android/mobile).
• Expertise in machine learning frameworks (TensorFlow, PyTorch).
• Advanced proficiency in Python or Kotlin.
• Experience with data processing and pipeline tools (e.g., Spark, Hadoop, Airflow).
• Understanding of A/B testing, causal inference, experimentation methods.
• Familiarity with ad tech, programmatic advertising, and user behavior metrics.
• Excellent analytical, problem-solving, and communication skills.

Nice to Haves:

• Experience optimizing ad displays on native mobile or Android surfaces.
• Familiarity with the Android SDK and native Android UI/UX design principles.
• Knowledge of reinforcement learning, multi-armed bandit models, contextual targeting.

Location: Bengaluru, India (hybrid or 100% remote).

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