Head of Data Science - Embeddings & Commercial AI

Jobtome; Breezy Point (MN); On-site; USD 263,000 - 394,000; Full time

Job summary

Spectrum is seeking a Head of Data Science to lead predictive modeling, embedding development, and measurement for its AI-native platform. You will build and scale models on household data, define evaluation standards, and collaborate with product, sales, and external partners to drive revenue impact.

You will lead a diverse data science team, establish MLOps, privacy, and governance practices, and align the embedding roadmap with commercial objectives across audiences, findings, and licenses.

Qualifications

• Bachelor's Degree in Computer Science, Statistics, Mathematics, Economics, or a related quantitative field.
• Experience leading applied data science or analytics teams that build data, audience, identity, or measurement products: 7 years.
• Hands-on predictive modeling, machine learning, or measurement science on consumer-level data at scale: 10 years.

Responsibilities

• Build predictive models behind the platform's audiences and data products across multiple verticals.
• Lead the embedding model roadmap and ensure commercial relevance and measurable lift.
• Define evaluation standards, MLOps, and responsible AI practices for deployed models.
• Manage a data science team and collaborate with product, sales, and external partners to scope pilots and evaluations.

Skills

Lead data science

Predictive modeling

MLOps

Transformer models

Privacy & ethics

Team leadership

Education

Bachelor's in CS/Stats

Tools

Python

SQL

Spark

Snowflake

Databricks

AWS

Job description

Spectrum is seeking a Head of Data Science to lead predictive modeling, embedding development, and measurement for its AI-native platform. You will build and scale models on household data, define evaluation standards, and collaborate with product, sales, and external partners to drive revenue impact.

You will lead a diverse data science team, establish MLOps, privacy, and governance practices, and align the embedding roadmap with commercial objectives across audiences, findings, and licenses.