Lead Data Scientist

Digital Turbine•Durham, CA

2d•Hybrid

About The Position

The Lead Data Scientist serves as a technical authority and strategic partner across engineering, product, and executive leadership. The role defines technical roadmaps, architects high-impact machine-learning and experimentation frameworks, and sets standards for data science across DT’s global marketplace, including bidding efficacy, audience curation, measurement, attribution, and enterprise experimentation. The position leads the translation of complex business challenges into multi-quarter data science roadmaps that shape product strategy and commercial outcomes. It drives the methodology, design, and standards for DT’s centralized experimentation framework, and architects production-grade statistical and machine-learning models for bid optimization, audience segmentation, recommendations, and user lifetime value. The role also directs time-series forecasting and real-time event-processing strategy, partners with ML Infrastructure and Data Engineering on productionization practices, and applies advances in the field to DT’s systems at scale. The Lead Data Scientist elevates technical rigor by mentoring senior and mid-level scientists, establishing code and methodology standards, and promoting evidence-based decision-making. The role also communicates technical findings as strategic narratives for executive stakeholders and influences product strategy and commercial investments.

Requirements

At least 8 years of progressive experience in data science, quantitative analysis, or machine learning.
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, including regression, classification, time-series, clustering, deep learning, multi-armed bandits, and large language models.
Expert fluency in Python or R and SQL.
Experience handling petabyte-scale data within cloud infrastructure, such as Databricks, Spark, Snowflake, AWS, or GCP.
Ability 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 auctions, algorithmic yield management, large-scale content recommendation engines, or advanced attribution methodologies.
Master’s or Ph.D. in a quantitative field such as Statistics, Computer Science, Economics, Operations Research, or Physics.

Responsibilities

Translate complex business challenges into multi-quarter data science roadmaps that shape product strategy and commercial outcomes.
Lead methodology, design, and standards for DT’s centralized experimentation framework, including causal inference, A/B testing, and hypothesis validation.
Architect and oversee production-grade statistical and machine-learning models for bid optimization, audience segmentation, recommendations, and user lifetime value.
Direct strategy for time-series forecasting and real-time event processing to improve automated decision-making, yield optimization, and recommendation engines.
Partner with ML Infrastructure and Data Engineering leads to establish best practices for model deployment, monitoring, and integration.
Apply state-of-the-art advances to DT’s systems in a strategic and scalable manner.
Mentor senior and mid-level data scientists, establish code and methodology standards, and foster an evidence-based culture.
Translate technical findings into strategic narratives for executive stakeholders and influence product strategy and commercial investments.