Director of Data Science & Embeddings Platform

Jobtome; Village of New Hyde Park (NY); On-site; USD 263,000 - 394,000; Full time

Job summary

Spectrum seeks a Head of Data Science to lead a bold, AI-native behavioral platform. You’ll build predictive models, steer the embedding layer, and set evidence standards for buyers, collaborating with product, sales, and external partners to scope pilots and convert evaluations into contracts.

You will oversee a team of data scientists and ML engineers, drive responsible AI practices, and ensure production deployment with measurable revenue impact.

Qualifications

• Experience leading applied data science or analytics teams.
• Hands-on predictive modelling on consumer data at scale.
• Strong grounding in measurement and causal inference.
• Proficiency with Python, SQL, and distributed processing (Spark).
• Experience with privacy-preserving analytics and data collaboration.

Responsibilities

• Build and lead predictive models behind data products and audiences.
• Own the embedding model roadmap and evaluation standards.
• Define and defend measurement methodologies for buyers.
• Lead the data science team growth and hiring strategy.
• Scope pilots with product and sales, translating methodology into contracts.
• Collaborate with Legal and Privacy on responsible AI practices.

Skills

Data science leadership

Predictive modeling at scale

Measurement & causal inference

Python & SQL

Distributed processing (Spark)

Executive communication

Team building & hiring

AI applications curiosity

Education

Bachelor's degree in CS/Stats/Math/Economics

Master's or PhD preferred

Tools

Snowflake

Databricks

AWS

LiveRamp

RampID/UID2

Job description

Spectrum seeks a Head of Data Science to lead a bold, AI-native behavioral platform. You’ll build predictive models, steer the embedding layer, and set evidence standards for buyers, collaborating with product, sales, and external partners to scope pilots and convert evaluations into contracts.

You will oversee a team of data scientists and ML engineers, drive responsible AI practices, and ensure production deployment with measurable revenue impact.