1

Manager Data Analyst Machine Learning Jobs in California

Lead the continuous quality management of ML datasets, with a specific focus on human-generated ... Experience in data analysis, data engineering, and machine learning data operations. * Experience ...

SR Data Analyst

San Francisco, CA

$101.30K - $127.80K/yr

... Machine Learning, Deep Learning, NLP, and Simulation in an agile development framework. • Conduct quantitative analysis of experimental, and textual data to generate insights and drive decision ...

In this role, you will apply advanced data analytics and machine learning techniques to explore ... Experience working with Atlassian product and project management tools (Jira, Confluence) * If ...

Analyzing the Data: Work closely with product managers, data scientists, and engineers to find opportunities for applying machine learning to drive business impact and enhance Strava's features and ...

We are looking for a curious and innovative Machine Learning Engineer to explore, experiment and build AI driven solutions that analyze customer journey and go to market data. The ideal candidate ...

Data Science Manager

Irvine, CA · On-site

$119.13K - $197.73K/yr

This individual has expertise in machine learning, statistical modeling, and data visualization to ... analysis methods * Foster a high-performing team culture through coaching, mentoring, and ...

Perform advanced analysis and develop tools to advance digital transformation efforts ... Use and mentor others in methods of data management, operations research, machine learning ...

This individual has expertise in machine learning, statistical modeling, and data visualization to ... analysis methods * Foster a high-performing team culture through coaching, mentoring, and ...

... analysis, data engineering, and machine learning data operations. Experience designing data quality control processes, data curation workflows, or Human-in-the-Loop initiatives. Experience managing ...

next page

Showing results 1-20

Manager Data Analyst Machine Learning information

What is the difference between Manager Data Analyst Machine Learning vs Data Scientist?

AspectManager Data Analyst Machine LearningData Scientist
Required CredentialsBachelor's/Master's in Data Science, Analytics, or related; experience in machine learningBachelor's/Master's/PhD in Data Science, Statistics, or related; strong programming skills
Work EnvironmentTeam leadership, project management, cross-department collaborationResearch, model development, data exploration
Employer & Industry UsageBusiness analytics, tech companies, finance, healthcareTech firms, research institutions, consulting

While both roles involve data analysis and machine learning, the Manager Data Analyst Machine Learning focuses on leading teams and managing projects, whereas Data Scientists primarily develop models and perform in-depth data research.

What are the most commonly searched types of Data Analyst Machine Learning jobs in California? The most popular types of Data Analyst Machine Learning jobs in California are:
What cities in California are hiring for Manager Data Analyst Machine Learning jobs? Cities in California with the most Manager Data Analyst Machine Learning job openings:

Machine Learning Engineer

RZR Global Inc.

San Francisco, CA • On-site

Full-time

Posted 12 days ago


Job description

Who are we?RZR Global is an AI-driven company specializing in mobile advertising solutions designed to fuel revenue growth. We leverage AI to discover audiences in a privacy-first environment through trillions of contextual bidding signals and proprietary behavioral models. Our audience engagement platform includes creative strategy and execution. We handle 5 million mobile ad requests per second from over 10 billion devices, driving performance for both publishers and brands. We are headquartered in San Francisco, CA, with a global presence across the United States, EMEA, and APAC.
Role Overview
We are seeking a motivated and detail-oriented Machine Learning Engineer to join our team. As an ML Engineer, you will be involved in designing and implementing machine learning models and data pipelines to enhance our programmatic demand-side platform (DSP). You will work closely with Senior MLE and other team members to drive impactful machine learning projects and contribute to innovative solutions.
Key Responsibilities
  • Support the development of machine learning models to address challenges in programmatic advertising, such as predicting user responses, forecasting bid landscapes, and detecting fraud.
  • Collaborate with senior data scientists and cross-functional teams (product, engineering, and analytics) to integrate models into production workflows.
  • Analyze the impact of integrating new data sources and features into our models.
  • Build and maintain data pipelines to process and prepare large datasets for model training and evaluation.
  • Contribute ideas and assist in testing new tools, methodologies, and technologies to improve our machine learning capabilities.
  • Document experiments, assumptions, and outcomes; maintain reproducibility
Required Skills / Experience
  • Bachelor's or Master's degree in Mathematics, Physics, Computer Science, or a related technical field.
  • At least 1 year of professional experience in machine learning, statistical analysis, and data analysis.
  • Experience with machine learning techniques such as regression, classification, and clustering.
  • Proficiency in Python and SQL and familiarity with big data tools (e.g., Spark) and ML libraries (e.g., TensorFlow, PyTorch, Scikit-Learn).
  • Strong grasp of probability, statistics, and data analysis principles.
  • Ability to work effectively in a team environment, with good communication skills to explain complex concepts to diverse stakeholders.
Nice-to-Have
  • Familiarity with system programming languages including C++ and Rust is a plus.
  • Exposure to online inference systems, gRPC/REST model endpoints, or streaming features (Kafka/Flink)
  • Ad-tech familiarity: auction dynamics, pacing, fraud signals, creative personalization.