Senior/Staff Data Scientist

Sovrn•Boulder, CO

4d•Hybrid

About The Position

The Senior or Staff Data Scientist at Sovrn is a strategic technical leader who drives high-impact data initiatives across the organization. This role requires a unique blend of strategic vision ("the art of the possible") and tactical execution ("think and execute tactically"). You will solve ambiguous problems to deliver measurable business value and influence the future of data science in AdTech. You will guide architecture, experimentation, and product design while ensuring scientific and technical rigor. This position requires technical excellence, business acumen, and leadership. Operate at the frontier of applied AI as a hands-on practitioner — personally exploring, prototyping, evaluating, and deploying cutting-edge Agentic AI technologies to bring new, grounded insight into product, modeling, and optimization decisions. Design, implement, and deploy advanced predictive models, algorithms, and experiments that drive core business outcomes, specifically optimizing ad performance, user engagement, and monetization strategies. Analyze large-scale datasets to identify patterns, trends, and optimization opportunities, while maintaining and improving data pipelines and experimentation frameworks to support ongoing research. Define and evangelize best practices in modeling, testing, measurement, and data governance, ensuring scientific rigor while balancing practical business constraints and timelines. Collaborate cross-functionally to identify high-leverage AI/ML opportunities, translate them into actionable roadmaps, and deliver measurable outcomes. Mentor other data scientists through code review, experiment design, and technical feedback and model what it looks like to stay current with fast-moving AI research in a production-focused environment. Translate complex data problems into strategic decisions through compelling storytelling and clear communication of findings and recommendations to both technical and non-technical stakeholders.

Requirements

Proven ability to deploy autonomous agents to production including optimizing inference performance, managing latency/cost tradeoffs, and ensuring robust model evaluation at each stage.
Demonstrates prompt engineering discipline by systematically evaluating prompts across model families using quantitative evaluation.
Active use of AI tools in daily technical work — integrates coding assistants, orchestration frameworks, and search-augmented workflows in ways that accelerate prototyping and reduce iteration cycles.
Experience in data science and applied machine learning, with a strong record of delivering business impact.
Deep technical proficiency across classical ML (supervised/unsupervised), reinforcement learning, and deep learning, with a strong foundation in statistical modeling, A/B testing, and causal inference.
Proficiency with Python, SQL, cloud-based data ecosystems, modern AI/ML frameworks (e.g., Pandas, Scikit-Learn, PyTorch/TensorFlow, LangChain, Hugging Face, PydanticAI), experience with large-scale datasets and distributed computing frameworks (e.g., Spark, PySpark), and Databricks running on AWS Cloud.
Demonstrated ability to influence across technical and non-technical audiences, lead cross-functional initiatives, and work in a collaborative, agile environment (Scrum and Kanban).
A strategic mindset with strong product intuition and experience working at scale.
Accountability and the ability to thrive in a fluid and collaborative environment.

Nice To Haves

Strong understanding of ad tech ecosystems (e.g., RTB, DSP/SSP workflows, auctions, and yield optimization) is a big plus.

Responsibilities

Operate at the frontier of applied AI as a hands-on practitioner — personally exploring, prototyping, evaluating, and deploying cutting-edge Agentic AI technologies to bring new, grounded insight into product, modeling, and optimization decisions.
Design, implement, and deploy advanced predictive models, algorithms, and experiments that drive core business outcomes, specifically optimizing ad performance, user engagement, and monetization strategies.
Analyze large-scale datasets to identify patterns, trends, and optimization opportunities, while maintaining and improving data pipelines and experimentation frameworks to support ongoing research.
Define and evangelize best practices in modeling, testing, measurement, and data governance, ensuring scientific rigor while balancing practical business constraints and timelines.
Collaborate cross-functionally to identify high-leverage AI/ML opportunities, translate them into actionable roadmaps, and deliver measurable outcomes.
Mentor other data scientists through code review, experiment design, and technical feedback and model what it looks like to stay current with fast-moving AI research in a production-focused environment.
Translate complex data problems into strategic decisions through compelling storytelling and clear communication of findings and recommendations to both technical and non-technical stakeholders.