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Manager Data Analyst Machine Learning Jobs in California

Sift

Machine Learning Engineer

San Francisco, CA · On-site

$160 - $230/hr

Work cross-functionally with Core Infrastructure, Product Management, and Data Science teams to ... analysis and model prototyping). * Distributed Systems & Big Data: Practical experience with ...

BeeGenius

AI/Machine Learning Engineer

Sunnyvale, CA · On-site

... analysis and data visualization techniques • Understanding of deep learning architectures and algorithms • Excellent problem-solving and critical-thinking skills Company : We are a leading ...

Adobe, Inc.

Machine Learning Engineer

San Jose, CA · On-site

Computer Science, Data Science, AI/ML Engineering, or a related field * Strong analytical and ... Manager, and GenStudio enable people and businesses to turn ideas into impact, powered by AI and ...

Adobe

Machine Learning Engineer

San Jose, CA

Computer Science, Data Science, AI/ML Engineering, or a related field * Strong analytical and ... Manager, and GenStudio enable people and businesses to turn ideas into impact, powered by AI and ...

Cliftonlarsonallen

Data Analyst Senior

Sacramento, CA · On-site +1

$107K - $155K/yr

The Data Analyst Senior cleans up data for analysis and/or visual display in a report or dashboard ... Develop predictive analytics, machine learning, and artificial intelligence techniques. Use ...

Umba

Your primary focus will be in applying data mining techniques, doing statistical analysis, and ... We train and deploy new machine learning models regularly and subscribe to data driven decision ...

Umba

Machine Learning Lead

San Francisco, CA · On-site

Your primary focus will be in applying data mining techniques, doing statistical analysis, and ... We train and deploy new machine learning models regularly and subscribe to data driven decision ...

Apple

Machine Learning - Data Scientist

Sunnyvale, CA · On-site

The Video Engineering Data Analytics and Quality group is seeking an expert in evaluating machine learning and deep learning models, including foundation models and multimodal systems. This role will ...

Articul8

Machine Learning Engineer

Dublin, CA · On-site

$90 - $130/hr

... and data analysis. You will own all work related to acquiring high-quality data to power the ... Experience in machine learning projects in text or vision, e.g., has trained machine learning ...

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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:

Manager Data Analyst Machine Learning jobs near you

Machine Learning Engineer

Sift

San Francisco, CA • On-site

$160 - $230/hr

Other

Posted 24 days ago

Job description

The Role:

As a Machine Learning Engineer at Sift, you will bridge the gap between data science and large-scale distributed systems. You won’t just train models in isolation; you will build end-to-end pipelines that extract signals, train custom models per merchant, and serve predictions at production scale with low latency. You will work on an automated machine learning ecosystem that dynamically recalibrates models based on streaming global telemetry data.

What You'll Do:
  • Model Development & Refinement: Design, build, and deploy online machine learning models (including ensemble methods, deep learning, transformer architectures and graph-based models) to catch evolving fraud vectors in real time.
  • Feature Engineering at Scale: Engineer high-frequency time-series features from over 1 trillion behavioral events, optimizing for low-latency signal extraction and pattern recognition.
  • Production MLOps: Maintain and enhance our automated model training and deployment infrastructure, ensuring frictionless continuous integration and continuous deployment (CI/CD) of newly trained models.
  • System Optimization: Write high-performance code to minimize scoring latency at runtime, ensuring our core ML services scale seamlessly across distributed databases.
  • Collaborative Innovation: Work cross-functionally with Core Infrastructure, Product Management, and Data Science teams to translate business-level fraud patterns into robust algorithmic solutions.
What We Are Looking For (Requirements):
  • Experience: 4+ years of professional experience building and deploying large-scale machine learning models into high-traffic production environments.
  • Solid Programming Foundations: Strong proficiency in Java or Scala (for our production backend) as well as Python (for data analysis and model prototyping).
  • Distributed Systems & Big Data: Practical experience with Databricks and big data processing frameworks like Apache Spark, Apache Flink, or Hadoop, and working with NoSQL data stores like Bigtable.
  • Strong Mathematical Foundations: Deep understanding of statistical modeling, probability, and standard machine learning algorithms (e.g., XGBoost, Random Forests, Neural Networks, and Clustering techniques).
  • System Design Mentality: Ability to reason through data consistency, pipeline failures, and performance constraints in a distributed, multi-tenant cloud environment (GCP).
Bonus Points (Preferred Qualifications):
  • Experience explicitly in the fraud detection, risk mitigation, or cyber-security domains.
  • Deep knowledge of streaming architectures (e.g., Apache Kafka).
  • Familiarity with containerization and orchestration tools like Docker and Kubernetes.
  • Familiarity with leveraging AI coding assistants (e.g., Claude Code) to accelerate development and model prototyping

Please note: final stage candidates may be asked to travel for in-person final round interviews.

Let's build it together:

At Sift, we are intentionally building a diverse, equitable, and inclusive workplace. We believe that diversity drives innovation, equity is a fundamental right, and inclusion is a basic human need. We envision a place where all Sifties feel secure sharing their authentic selves and diverse experiences with their teams, their customers, and their community – ultimately using this empowerment and authenticity to build trust and create a safer Internet.

—This document provides transparency around how Sift handles the personal data of job applicants: https://sift.com/recruitment-privacy

A little about us:

Sift is the AI-powered fraud platform securing digital trust for leading global businesses. Our deep investments in machine learning and user identity, a data network scoring 1 trillion events per year, and a commitment to long-term customer success empower more than 700 customers to grow fearlessly. Global brands rely on Sift to unlock growth and deliver seamless consumer experiences. Visit us at sift.com and follow us on LinkedIn.

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