Principal Data Scientist

About the role

At Digital Turbine, we make mobile advertising experiences more meaningful and rewarding for users, app publishers, and advertisers - intelligently connecting people in more ways, across more devices. We provide app publishers and advertisers with powerful ads and experiences that captivate consumers, fuel performance, and help telecoms and OEMs supercharge awareness, acquisition, and monetization. In a rapidly evolving industry, we are constantly innovating and creating better paths of discovery to connect consumers, publishers, and advertisers across the mobile ecosystem. Please note that Digital Turbine is a hybrid work environment-only candidates local to the posting location will be considered. Data Science at Digital Turbine mixes deep technical expertise with creativity in one of the most dynamic domains: mobile advertising Our platforms operate at global scale, where even a small improvement in model performance can translate into outsized revenue gains and better user experiences Principal Data Scientists shape the long‑term modeling strategy, lead high‑impact initiatives, and define the standards for data science excellence across the company About the Principal Data Scientist Acts as a top‑level expert and thought leader in data science, responsible for strategic direction of modeling and experimentation in key product areas Tackles problems of the highest complexity and impact, where solutions require inventing or significantly extending current approaches Defines methodologies, standards, and frameworks that are adopted by data science teams company‑wide Bridges technical depth with business acumen to influence product strategy and company‑level decisions Exercises expert judgment in selecting and creating analytical approaches, often in areas with limited precedent or ambiguous requirements Operates with substantial independence; work is primarily reviewed in terms of long‑term impact and alignment with business strategy Serves as a trusted advisor to senior leaders across Product, Engineering, and the business, frequently shaping roadmaps and strategic priorities Mentors Senior and Lead Data Scientists, building capabilities, reviewing critical work, and setting the bar for scientific rigor and impact Communicates complex statistical and machine learning concepts in a clear, compelling way to audiences ranging from engineers to executives Define the long‑term modeling strategy for core areas such as user lifetime value prediction, recommendation and personalization, real‑time bidding and pricing, fraud detection, and creative optimization Lead the design and development of novel machine learning and statistical approaches, including probabilistic graphical models, Bayesian methods, deep learning architectures, and advanced decisioning systems Architect end‑to‑end data science solutions, from problem framing and feature strategy through model training, evaluation, deployment, and monitoring in collaboration with Data Engineering and Product Engineering Establish and evolve experimentation and causal inference frameworks (eg, A/B testing, multi‑armed bandits, uplift modeling) to robustly measure the impact of product and policy changes Drive high‑visibility, cross‑functional initiatives that leverage data science to unlock new product capabilities or revenue streams, often spanning multiple teams and business units Set best practices and standards for modeling, code quality, documentation, reproducibility, and ethical AI considerations Identify and prioritize new data sources and signals that can materially improve model performance or unlock new use cases Represent Digital Turbine's data science capabilities externally as needed (eg, technical talks, publications, partner discussions), in alignment with company strategy Proven hands-on experience operating within a marketplace adtech environment - spanning demand-side optimization, supply monetization, and data pipeline architecture at scale Direct ownership of systems or products that sit across the full adtech stack: bidding infrastructure, yield management, audience data, and measurement About you as the Principal Data Scientist: Typically requires 12+ years of related experience Deep, demonstrable expertise in machine learning and statistics, including several of the following: forecasting, large‑scale recommendation systems, information retrieval, probabilistic graphical models, Bayesian inference, causal inference, and deep learning Extensive experience developing and deploying production machine learning systems at scale, including both batch and real‑time/online models Strong software engineering skills and demonstrated experience building production‑quality ML code and pipelines (version control, testing, CI/CD) Proficiency in Python (or similar) for data science and strong SQL skills; experience with major ML and numerical libraries (eg, PyTorch, TensorFlow, JAX) Solid understanding of big data ecosystems (

Your Match

How well this role fits your profile.

Company Intel

What employees say

Worked at digitalturbine? Share your experience