Head of Data Science — Embeddings & Measurement

Jobtome; Village of Dobbs Ferry (NY); On-site; USD 263,000 - 394,000; Full time

Job summary

Spectrum is seeking a Head of Data Science to lead a team building AI-driven behavioral embeddings and predictive models across audiences and data products. You will own measurement methodology, feature stores, and scale workflows with privacy and governance in mind.

You will collaborate with technology partners and sales to drive pilots to contracts. You will lead a high-performance data science team, set hiring priorities, and establish repeatable workflows for trust, accuracy, and commercial

Qualifications

• Proven record leading data science for data, audience, identity, or measurement products.
• Hands‑on predictive modeling on consumer‑level data at scale with a focus on accuracy and commercial impact.
• Strong grounding in measurement, causal inference, and attribution limits.
• Experience with data collaboration ecosystems, identity graphs, and clean rooms.

Responsibilities

• Build and lead predictive models for audience and data products across multiple verticals.
• Own platform features like the Feature Store and audience definitions with measurable accuracy.
• Lead the embedding model roadmap and MLOps standards, including privacy and bias controls.
• Manage a team of data scientists and ML engineers toward production deployment and revenue impact.

Skills

Data science leadership

Predictive modeling

Measurement science

Identity and data collaboration

Education

Bachelor's degree in quantitative field

Tools

Python

SQL

Spark

Snowflake

Databricks

AWS

Job description

Spectrum is seeking a Head of Data Science to lead a team building AI-driven behavioral embeddings and predictive models across audiences and data products. You will own measurement methodology, feature stores, and scale workflows with privacy and governance in mind.

You will collaborate with technology partners and sales to drive pilots to contracts. You will lead a high-performance data science team, set hiring priorities, and establish repeatable workflows for trust, accuracy, and commercial