Top Data Science Jobs in San Diego, CA

As a Senior Data Scientist at Rocket Money, you'll build machine learning-driven features in collaboration with engineering, product, and design teams. You'll also optimize product feedback loops, develop automation for budgeting tools, construct customer segmentation models, run experiments to gauge impact, and ensure accuracy in transaction categorization systems. You'll lead projects, drive strategy, and focus on enhancing customers' financial experiences.

Develop predictive models and machine learning algorithms to optimize financial processes. Conduct exploratory data analysis to identify patterns, collaborate with teams to design analytical solutions, and build the Data Analytics function as the company expands.

The Soil Scientist will advise clients on carbon removal strategies, conduct due diligence on soil carbon projects, and contribute to science and policy efforts to mitigate climate change through soil management. This role requires a passion for carbon management and flexibility to implement strategies and deliver results.

The Research Scientist leads mixed-methods research to enhance student outcomes by integrating qualitative and quantitative methodologies. Responsibilities include designing studies, analyzing data, supervising analysts, and mentoring team members. The role involves collaboration with educational stakeholders and staying current with advancements in research.

As the Lead Data Scientist, you will establish a data science practice, collaborate with various teams to optimize systems, leverage data for strategic decisions, and utilize advanced analytics techniques to derive insights that drive business outcomes.

The Salesforce Administrator will ensure the accuracy and organization of Salesforce data, perform audits, implement data validation, and create reports and dashboards to support business insights and decision-making.

As a Staff LLM Data Scientist, you will design and fine-tune LLMs, build advanced Retrieval Augmented Generation pipelines, implement evaluation frameworks, and deploy models into production. A strong focus on generative AI applications in investment is needed, alongside collaboration with product and engineering teams.