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Data Analyst Data Science Jobs in California (NOW HIRING)

Guru Schools

Data Analyst/Data Modeler:

Sacramento, CA · On-site

$58.50 - $75.75/hr

Performs business and systems analysis and documentation- Develops conceptual and dimensional data models for the enterprise data warehouse- Performs data modeling in relational and dimensional ...

Brex

Data Analyst III

San Francisco, CA · On-site +1

$114K - $142K/yr

Our Data Scientists, Analysts, and Engineers work together to make data-and insights derived from data-a core asset across the company. What you'll do As a senior Data Analyst (DA III), you will own ...

LLNL

Data Science Engineer

Livermore, CA · On-site

$134K - $161K/yr

Analyze data and build analytical capabilities to improve the reliability and adversarial resilience of critical infrastructure. * Write code to implement and deploy data science solutions and ...

LLNL

Data Science Engineer

Livermore, CA

$134K - $161K/yr

Analyze data and build analytical capabilities to improve the reliability and adversarial resilience of critical infrastructure. * Write code to implement and deploy data science solutions and ...

LLNL

Data Science Engineer

Livermore, CA · On-site

$134K - $161K/yr

Analyze data and build analytical capabilities to improve the reliability and adversarial resilience of critical infrastructure. * Write code to implement and deploy data science solutions and ...

FocusKPI Inc.

Data Analyst

San Francisco, CA · On-site

The Data Analyst will leverage data on our users to build dashboards, answer key business questions ... Master's degree in computer science or any related field is required; Bachelor's degree in any ...

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Data Analyst Data Science information

Is 40 too late for data science?

Data analysts and data scientists can start their careers at any age, including 40 or older. Success in data science depends on acquiring relevant skills such as programming, statistics, and tools like Python or R, which can be learned at any stage of life. Many professionals transition into data roles later in their careers with dedication and continuous learning.

How do Data Analysts in Data Science typically collaborate with other departments or teams?

Data Analysts in Data Science frequently work cross-functionally, partnering with teams such as engineering, product management, marketing, and business intelligence. They translate complex data findings into actionable insights and tailor their communication to both technical and non-technical stakeholders. Regular collaboration may involve participating in meetings to understand business needs, designing dashboards for different teams, and providing data-driven recommendations to support company objectives. This collaborative environment not only enhances project outcomes but also fosters continuous learning and professional growth.

What is the 80 20 rule in data science?

In data science, the 80/20 rule, also known as the Pareto principle, suggests that roughly 80% of the results come from 20% of the efforts or data. Data analysts often use this concept to focus on the most impactful variables or features during analysis and modeling to improve efficiency and accuracy.

What does a Data Analyst in Data Science do?

A Data Analyst in Data Science collects, processes, and analyzes large sets of data to help organizations make informed decisions. They use statistical techniques and data visualization tools to identify trends, patterns, and insights from data. Their responsibilities often include cleaning data, creating reports, and communicating findings to stakeholders. Data Analysts play a key role in helping businesses optimize operations, understand customer behavior, and solve complex problems using data-driven approaches.

Can data science work as a data analyst?

Data science and data analysis are related fields, but they have different focuses. Data scientists often develop models and algorithms using programming languages like Python or R, while data analysts primarily interpret data, generate reports, and use tools like Excel or SQL. Skills in statistical analysis, data visualization, and understanding business needs are essential for both roles, and some professionals transition between them based on experience and training.

What is the difference between Data Analyst Data Science vs Data Engineer?

AspectData Analyst Data ScienceData Engineer
Required SkillsStatistics, programming (Python, R), data visualizationDatabase systems, ETL pipelines, programming (Python, Java)
Work EnvironmentAnalyzing data, building models, reportingBuilding and maintaining data infrastructure
CertificationsData Science certifications, SQL, PythonCloud certifications, database management
Industry UsageBusiness analysis, predictive modelingData infrastructure, big data systems

Data Analyst Data Science focuses on analyzing data and creating models to inform decisions, while Data Engineers build the systems that collect, store, and process data. Both roles require programming skills and often overlap in tools like Python and SQL, but their core responsibilities differ significantly.

What are the key skills and qualifications needed to thrive as a Data Analyst in Data Science, and why are they important?

To thrive as a Data Analyst in Data Science, you need strong analytical skills, proficiency in statistics, and a relevant degree such as in mathematics, computer science, or a related field. Familiarity with tools like SQL, Python or R, and data visualization platforms such as Tableau or Power BI, along with industry-recognized certifications, is highly valued. Attention to detail, problem-solving abilities, and effective communication skills help you interpret data insights and convey findings to stakeholders. These skills are crucial for transforming raw data into actionable intelligence that drives strategic business decisions.

Is AI replacing data analysts?

AI is transforming the role of data analysts by automating routine tasks such as data cleaning and basic analysis, allowing analysts to focus on more complex insights and strategic decision-making. While AI tools can augment their work, human expertise remains essential for interpreting results, understanding context, and communicating findings effectively. Data analysts who develop skills in machine learning, programming, and data visualization will continue to be valuable in the evolving data science environment.
What are popular job titles related to Data Analyst Data Science jobs in California? For Data Analyst Data Science jobs in California, the most frequently searched job titles are:
What job categories do people searching Data Analyst Data Science jobs in California look for? The top searched job categories for Data Analyst Data Science jobs in California are:
What cities in California are hiring for Data Analyst Data Science jobs? Cities in California with the most Data Analyst Data Science job openings:
Data Analyst Data Science jobs near you

Data Analyst/Data Modeler:

Guru Schools

Sacramento, CA • On-site

$58.50 - $75.75/hr

Full-time

Posted 10 days ago

Job description

Overview:
Key Duties/Responsibilities:Performs business and systems analysis and documentation- Develops conceptual and dimensional data models for the enterprise data warehouse- Performs data modeling in relational and dimensional models- Develops physical data model and/or works with architect to develop physical data model- Develops Data Facts and Dimensions in the EDW- Provides documentation to support the Kimball Dimensional Data Modeling Framework- Visualizes and designs the enterprise data management framework: specifies processes used to plan, acquire, maintain, use, archive, retrieve, control, and purge data- Documents data flow diagrams in existing and future reports to use as input in report design and optimization- Develops Requirements Specifications- Develops Design Specifications- Performs data analysis/predictive data modeling- Mentors and educates team members on best practices and industry standards
Mandatory:- Experience as a data analyst or in other quantitative analysis or related disciplines, such as researcher or data engineer supportive of key duties/responsibilities identified above.- Relevant experience in relational data modeling and dimensional data modeling, statistical analysis, and machine learning supportive of key duties/responsibilities identified above.- Excellent communication and collaboration skills to work effectively with stakeholders and team members.Desired:- Expert level Kimball Dimensional Data Modeling experience- Expert level experience developing in Oracle SQL Developer or ER/Studio Data Architect for Oracle.

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