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# Lead Data Scientist

- **Company:** Digital Turbine, Inc.
- **Location:** Durham, NC, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** A/B Testing, Amazon Web Services, Cloud Computing, Cluster Analysis, Information Engineering, Python (Programming Language), Machine Learning, Recommender Systems, SQL Databases, Systems Architecture, Large Language Models, Snowflake, Apache Spark, Deep Learning, Data Layers, Yield Optimization, Information Technology, Real Time Data, Data Management, Machine Learning Operations, Databricks
- **Published:** November 5, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=aecd31338c23eb16

## About the Role

* Experience: 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
 * Technical Mastery: Expertise in advanced statistical modeling, causal inference, experimental design, and machine learning architectures (regression, classification, time-series, clustering, deep learning, multi-armed bandits, and large language models)
 * Stack & Scale: Expert fluency in Python (or R), SQL, and handling petabyte-scale data within cloud infrastructure (e.g., Databricks, Spark, Snowflake, AWS/GCP)
 * Systemic Thinking: Proven ability to look beyond individual analyses to design scalable tools, reusable metrics frameworks, and automated modeling pipelines
 * Cross-Functional Leadership: 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, large scale content recommendation engines, or advanced attribution methodologies
 * Masters or Ph.D. in a quantitative field (Statistics, Computer Science, Economics, Operations Research, or Physics)

## Description

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., * Strategic Problem Framing & Vision: Lead the translation of complex, 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 optimization, audience segmentation, recommendations, and user lifetime value (LTV)
 * 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
 * Practical Application: Proven ability to take theoretical state of the art advances and apply them to DT's engines in a strategic and scalable manner
 * 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