Job description

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. Please note that Digital Turbine is a hybrid work environment-only candidates local to the posting location will be considered. DT has every ingredient of a marketplace business — advertisers, publishers, OEMs, carriers, consumers, devices, and proprietary device-level data. Our job in Data Science is to turn that raw advantage into intelligence: the insights, models, and systems that optimize the marketplace and prove the value we create. 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. About You As the Lead Data Scientist: 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). At DT, we are committed to pay transparency and equitable compensation. (For New York based candidates) The salary range for this position is $223,000-$272,000 based on experience, skills, and qualifications. In addition to competitive pay, we offer a comprehensive benefits package, including stock options, unlimited PTO and performance based bonus. We believe in fostering an environment where employees are valued and compensated fairly for their contributions. About Digital Turbine: Digital Turbine (NASDAQ: APPS) powers superior mobile consumer experiences and results for the world’s leading telcos, advertisers and publishers. Our end-to-end platform uniquely simplifies the ability to supercharge awareness, acquisition and monetization — connecting our partners to more consumers, in more ways, across more devices. The company is headquartered in Austin, Texas, with global offices in New York, Los Angeles, San Francisco, London, Berlin, Singapore, Tel Aviv, and other cities around the world, serving top agency, app developer, and advertising markets. We are honored to have achieved numerous awards as an employer of choice, around the world, including: BuiltIn s Best Places to Work Awards in 2022, 2023 and 2024, DUNS 100 Best Places to Work in Tech for 2023 and 2024, and BDICode s 100 Best Companies to Work in 2024. Digital Turbine is an equal opportunity employer committed to exemplifying diversity and inclusion around the world. We welcome people of different backgrounds, experiences, abilities, and perspectives. We embed diversity in our mindset, products, and teams to empower an inclusive, equitable, and culturally fluent environment. Building and continuously fostering this culture within our teams makes us better collaborators, partners, and innovators. Digital Turbine will process the information you provide during the application process in accordance with the Digital Turbine Global Recruitment Privacy Notice .

Requirements

Must-haves

Please note that Digital Turbine is a hybrid work environment-only candidates local to the posting location will be considered.
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.
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.
Executive Influence: Translate deep technical findings into strategic narratives for executive stakeholders, influencing product strategy and commercial investments.
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-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).
At DT, we are committed to pay transparency and equitable compensation.

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Keywords to mirror

mlmachine learningpythonsqlawsgcp

Platform: workday
Effort: long

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