Lead Data Scientist

Digital Turbine•Durham, NC

17h•Hybrid

About The Position

DT is seeking a Lead Data Scientist to serve 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 data science standards across DT’s global marketplace, including bidding efficacy, audience curation, measurement, attribution, and enterprise experimentation. The Lead Data Scientist translates complex business challenges into multi-quarter data science roadmaps that shape product strategy and commercial outcomes. The role leads the methodology and standards for the centralized experimentation framework, including causal inference, A/B testing, and hypothesis validation. It oversees production-grade statistical and machine-learning models for bid optimization, audience segmentation, recommendations, and user lifetime value, as well as strategies for time-series forecasting and real-time event processing. Working with ML Infrastructure and Data Engineering leads, the role establishes practices for model deployment, monitoring, and integration. It also mentors senior and mid-level scientists, sets code and methodology standards, builds an evidence-based culture, and communicates technical findings to executive stakeholders to influence product strategy and commercial investment. DT describes its work environment as hybrid and flexible and offers competitive compensation, equity awards, and a comprehensive benefits package.

Requirements

At least 8 years of progressive experience in data science, quantitative analysis, or machine learning.
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, 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 the 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 enhance 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 advances in data science strategically and scalably to DT’s engines.
Mentor senior and mid-level 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.
Drive technical strategy across product, engineering, and business organizations.