Principal Data Scientist job at Digital Turbine in Austin, TX, New York, NY

Title: Principal Data Scientist

Locations:

United States - New York

United States - Austin

time type

Full time

job requisition id

JR100935

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

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 help mobile game developers and app publishers grow and monetize their user bases using cutting‑edge machine learning and data‑driven optimization

Please note that Digital Turbine is a hybrid work environment—only candidates local to the specified office locations will be considered

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,