Principal Data Scientist

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 (Spark, Hadoop, Databricks, BigQuery, etc) and experience working in modern cloud environments (AWS, GCP, or Azure)

• Proven track record of driving significant business impact through data science initiatives, with examples of leading projects from concept through production and measurable outcomes

• Strong leadership, collaboration, and influence skills, with experience guiding other data scientists and partnering with senior cross‑functional stakeholders

• Ability to synthesize complex technical and business information into clear recommendations and to shape product and platform roadmaps through data‑driven insights