Staff Data Scientist, Ads — Remote ML Leader

Life360; United States; On-site; USD 155,000 - 283,000; Full time

Benefits offered by this job

Competitive pay and benefits

Full medical, dental, vision, and life

401(k) with company match (US)

RRSP for Canadian employees

Flexible PTO and company-wide days off

Equipment & remote-work support

Life360 Platinum Membership

Job summary

Life360 is seeking a Staff Data Scientist for the Ads team to own deep analysis and translate findings into algorithmic solutions. You will collaborate with engineers to implement, deploy, and operate ML systems at scale within our ads infrastructure, taking models from prototype to production in a data-driven way.

You will work in a remote-first environment, with a strong emphasis on agentic AI tooling, on-call rotation, and cross-functional leadership to improve bid efficiency and latency

Qualifications

• Advanced degree in a quantitative field or equivalent experience.
• 8+ years of experience in ML/optimization systems.
• Strong proficiency in Python with software engineering practices.

Responsibilities

• Partner with Product, Data Science, Cloud Engineering, and Data Engineering to design, develop, and deploy ML and optimization solutions.
• Train, deploy, and scale ML models as microservices or batch workflows.
• Establish logging, monitoring, and performance metrics for models.

Skills

Python

ML lifecycle

Cloud foundations

Communication

Leadership

Education

Advanced degree in a quantitative field

Tools

MLflow

Kubeflow

SparkML

SQL

Airflow

Job description

Life360 is seeking a Staff Data Scientist for the Ads team to own deep analysis and translate findings into algorithmic solutions. You will collaborate with engineers to implement, deploy, and operate ML systems at scale within our ads infrastructure, taking models from prototype to production in a data-driven way.

You will work in a remote-first environment, with a strong emphasis on agentic AI tooling, on-call rotation, and cross-functional leadership to improve bid efficiency and latency