Staff Data Scientist

About the role

Join Life360's Ads team as a Staff Data Scientist to lead the design, development, and deployment of advanced machine learning and optimization solutions. In this role, you will be instrumental in enhancing the effectiveness, efficiency, and scalability of our advertising systems, which are a core revenue stream for the company. You will have the opportunity to own the end-to-end lifecycle of machine learning models, from experimentation and development to production deployment and monitoring. This position offers a unique chance to work within a fast-paced, AI-native environment, collaborating closely with cross-functional teams including product managers, engineers, and analysts. Your work will directly impact the company's growth and success by improving ad performance and user experience through sophisticated data-driven solutions.

Key facts

What you'll do

• Design, develop, and implement machine learning and optimization systems that improve advertising performance, working closely with product, engineering, and analytics teams to align solutions with business goals.
• Build, train, and deploy machine learning models as scalable production services or batch processes, ensuring they integrate seamlessly into the company's advertising infrastructure.
• Develop and maintain comprehensive monitoring, alerting, and logging systems to track model performance, system health, and data drift, enabling proactive management of deployed models.
• Implement robust data, code, and artifact lineage tracking practices to ensure reproducibility, transparency, and compliance with industry standards and regulations.
• Improve and optimize data infrastructure and pipelines to support experimentation, model training, and deployment workflows, facilitating rapid iteration and deployment cycles.
• Mentor junior data scientists and engineers, sharing best practices and fostering a culture of continuous learning and improvement within the team.
• Utilize agentic AI tools regularly for coding, implementation, review, and analysis tasks, leveraging automation to increase productivity and accuracy.
• Participate in an on-call rotation to address and resolve production incidents promptly, ensuring high system availability and reliability.
• Collaborate with cross-functional teams to translate business needs into technical solutions, providing insights and recommendations based on data analysis.
• Contribute to the development of scalable, maintainable, and efficient machine learning systems that can be used across multiple advertising products and platforms.
• Stay current with industry trends, emerging technologies, and best practices in machine learning, optimization, and data engineering, applying relevant innovations to improve existing systems.

Requirements

• An advanced degree in a quantitative field such as Computer Science, Data Science, Statistics, or related disciplines, or equivalent professional experience.
• A minimum of 8 years of experience analyzing, implementing, and operating machine learning or optimization systems in a production environment.
• Strong proficiency in Python, with a solid understanding of software engineering principles including testing, modularization, and version control.
• Extensive experience with machine learning lifecycle management and data processing tools such as MLflow, Kubeflow, SparkML, Synapse ML, SQL, Spark/PySpark, dbt, and Airflow.
• Practical experience working with major cloud platforms like AWS, GCP, or Databricks, including knowledge of cloud networking, security, and storage solutions.
• Proven ability to lead projects and influence cross-functional teams, demonstrating excellent communication and collaboration skills.
• Strong analytical skills with the ability to interpret complex data and translate insights into actionable solutions.
• Experience with deploying models at scale and maintaining production systems in a high-availability environment.
• Familiarity with data governance, compliance, and security standards relevant to cloud-based machine learning systems.
• Ability to work in a fast-paced environment, managing multiple priorities and deadlines effectively.

Nice to have

• Hands-on experience with solving optimization problems such as linear programming, mixed-integer programming, or other mathematical optimization techniques applied to advertising use cases like budget allocation, bid optimization, or audience targeting.
• Knowledge of advanced AI techniques, including agentic AI tools, to automate and enhance model development and deployment workflows.
• Experience working with large-scale data infrastructure and data lakes, especially in cloud environments.
• Familiarity with A/B testing frameworks and experimentation methodologies to validate model improvements.
• Understanding of privacy-preserving machine learning techniques and data anonymization practices, especially relevant for advertising data.

Skills & tools

• Python
• MLflow
• Kubeflow
• SparkML
• Synapse ML
• SQL
• Spark/PySpark
• dbt
• Airflow
• AWS, GCP, or Databricks
• Agentic AI tools (e.g., Claude Code)

Practical notes

• Salary range for US-based candidates: $155,000 - $283,000 USD.
• Salary range for Canada-based candidates: $224,500 - $262,000 CAD.
• Compensation package includes base salary, equity, and a comprehensive benefits plan.
• Benefits include medical, dental, vision, financial, and other insurance options.
• Retirement plans such as 401(k) with company match (US) or RRSP with DPSP (Canada).
• Flexible Paid Time Off (PTO) policies and 12 company holidays to support work-life balance.
• Support for remote work setup, including equipment and resources needed for effective remote collaboration.
• Investment in ongoing learning and development programs to keep skills current.
• Visa sponsorship is not available for this role, so candidates must have the legal right to work in the listed locations.