Top Senior Level Data Engineer Jobs in San Francisco Bay Area

Seeking a Director of Data Science to lead a team in leveraging data for business decisions, specifically in marketing. Responsibilities include developing predictive models, analyzing marketing performance, and driving data-informed strategies for growth and monetization. Must have a deep understanding of consumer lifecycle analysis and SaaS operations.

Design and build ML architectures and data pipelines for large-scale data repositories. Work on Clari's AI models and platform. Collaborate with stakeholders to deliver enterprise products. Fully remote opportunity in the United States.

Lead a team of Machine Learning Engineers in developing ML solutions across marketing, product, and operations. Collaborate with cross-functional teams to build machine-learning based systems and guide strategic decisions in machine learning and data use.

The Lead Data Specialist will supervise a team within Data Management, manage day-to-day operations, and interact with internal study teams and external end users to maintain exceptional database integrity.

Design, develop, and maintain scalable solutions for metrics, reports, and analyses to meet business needs. Collaborate with teams to measure customer experience and drive business outcomes. Troubleshoot data quality issues and recommend improvements to data sources. Have 5+ years of experience in the data/BI space and proficiency in SQL and data visualization tools like Tableau.

As a Tech Lead for Data at Forge, you will bring your substantial experience in data solutions to help unlock the power of our data for our customers. You will handle the increasing complexity and demand for data from various sources, enabling customers to make informed investment decisions. You will lead the data engineering team in designing and implementing a cutting-edge data platform and pipelines along with direct collaboration with crucial teams such as Product, Research, and Engineering enhance the data platform.

Develop and improve machine learning models for customer support experiences, utilizing state-of-the-art algorithms and large language models. Collaborate with cross-functional teams to meet business needs and monitor model performance for continuous improvement.