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# Lead Data Scientist

- **Company:** Digital Turbine, Inc.
- **Location:** New York, NY, United States
- **Experience:** Expert
- **Salary:** $223,000.0 - $272,000.0
- **Contract:** Permanent contract
- **Skills:** Amazon Web Services, Cloud Computing, Cluster Analysis, Python (Programming Language), Machine Learning, SQL Databases, Snowflake, Apache Spark, Deep Learning, Information Technology, Data Management, Databricks
- **Published:** October 2, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=baabd6e3b571d7a1

## 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, and deep learning).
 * 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 Have:

 * 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).

## Description

* 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.