Lead Data Scientist

Digital Turbine•Berlin, NY

4d•$223,000 - $272,000•Hybrid

About The Position

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. As a Lead Data Scientist, you will serve as a technical authority and strategic partner across engineering, product, and executive leadership. You will define technical roadmaps, architect high-impact ML and experimentation frameworks, and set the standard for data science excellence across DT’s global marketplace — from bidding efficacy and audience curation to measurement, attribution, and enterprise experimentation. About the Lead Data Scientist role: Strategic Problem Framing & Vision: Lead the translation of complex, high-ambiguity business challenges into multi-quarter data science roadmaps that directly shape product strategy and commercial outcomes. Experimentation Architecture: Drive the methodology, design, and standards for DT’s centralized experimentation framework — serving as the primary authority on causal inference, A/B testing, and hypothesis validation across product surfaces. Advanced Modeling & System Architecture: Architect and oversee the development of production-grade statistical and ML models for bid/UA optimization, dynamic audience segmentation, user lifetime value (LTV), and multi-touch attribution. Real-Time Data Intelligence: Direct the strategy for time-series forecasting and real-time event processing to enhance automated decisioning, yield optimization, and recommendation engines. Engineering Partnership & Productionization: Partner with ML Infrastructure and Data Engineering leads to establish best practices for model deployment, monitoring, and integration into DT’s Next Best Action framework. Technical Leadership & Mentorship: Elevate the technical rigor of the data science organization. Mentor senior and mid-level scientists, establish code and methodology standards, and foster an evidence-based culture across the company. Executive Influence: Translate deep technical findings into strategic narratives for executive stakeholders, influencing product strategy and commercial investments.

Requirements

8+ years of progressive experience in data science, quantitative analysis, or machine learning, with a track record of technical leadership in AdTech, two-sided marketplaces, or consumer-scale data platforms.
Expertise in advanced statistical modeling, causal inference, experimental design, and machine learning architectures (regression, classification, time-series, clustering, and deep learning).
Expert fluency in Python (or R), SQL, and handling petabyte-scale data within cloud infrastructure (e.g., Databricks, Spark, Snowflake, AWS/GCP).
Proven ability to look beyond individual analyses to design scalable tools, reusable metrics frameworks, and automated modeling pipelines.
Exceptional communication and alignment skills, with a history of driving technical strategy across matrixed product, engineering, and business organizations.

Nice To Haves

Hands-on experience architecting metrics semantic layers or enterprise experimentation tools.
Deep domain expertise in real-time bidding (RTB) auctions, algorithmic yield management, or advanced attribution methodologies.
Masters or Ph.D. in a quantitative field (Statistics, Computer Science, Economics, Operations Research, or Physics).

Responsibilities

Lead the translation of complex, high-ambiguity business challenges into multi-quarter data science roadmaps that directly shape product strategy and commercial outcomes.
Drive the methodology, design, and standards for DT’s centralized experimentation framework — serving as the primary authority on causal inference, A/B testing, and hypothesis validation across product surfaces.
Architect and oversee the development of production-grade statistical and ML models for bid/UA optimization, dynamic audience segmentation, user lifetime value (LTV), and multi-touch attribution.
Direct the strategy for time-series forecasting and real-time event processing to enhance automated decisioning, yield optimization, and recommendation engines.
Partner with ML Infrastructure and Data Engineering leads to establish best practices for model deployment, monitoring, and integration into DT’s Next Best Action framework.
Elevate the technical rigor of the data science organization. Mentor senior and mid-level scientists, establish code and methodology standards, and foster an evidence-based culture across the company.
Translate deep technical findings into strategic narratives for executive stakeholders, influencing product strategy and commercial investments.