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Data Science Engineer Jobs in California (NOW HIRING)

Collaborate with engineering, product, power system experts, and external partners to translate high-level business goals into rigorous data science roadmaps. * Executive Communication: Persuasively ...

The data science team is very much applied - their work directly makes its way into real products ... Data Engineering & Curation * Expertise in large-scale data collection, labeling, cleaning, and ...

Partner closely with Product, Growth, Engineering, and UX leadership to influence product roadmap ... Act as a thought leader on emerging data science techniques (personalization, recommendation ...

Collaborate with Data Scientists, Data Engineers, and Product teams on ongoing initiatives. * Document analyses, methodologies, and project findings. * Participate in team meetings, knowledge-sharing ...

Data Science Lead

Milpitas, CA ยท On-site +1

$148K - $168K/yr

Data Science Lead Job Code: A011.4794 Job Location: Milpitas, CA Job Type: Full-Time Rate of Pay ... Draw on a wide palette of optimization modeling, mathematics, statistics, AI/ML, and programming ...

We're looking for a Data Science Manager to lead our growing AI product data science function. This ... You'll partner closely with Product, Engineering, and Platform teams to deliver fast-moving ...

Data Science Manager

Los Angeles, CA ยท On-site

$160K - $220K/yr

We're looking for a Data Science Manager to lead our growing AI product data science function. This ... You'll partner closely with Product, Engineering, and Platform teams to deliver fast-moving ...

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Data Science Engineer information

See California salary details

$43.9K

$128K

$175.2K

How much do data science engineer jobs pay per year?

As of Jul 13, 2026, the average yearly pay for data science engineer in California is $128,018.00, according to ZipRecruiter salary data. Most workers in this role earn between $113,000.00 and $135,700.00 per year, depending on experience, location, and employer.

What engineers make 500,000?

Senior data science engineers, machine learning engineers, and software engineers with extensive experience and advanced skills in areas like AI, big data, and cloud computing can earn salaries of $500,000 or more, especially in high-cost-of-living regions or within top tech companies. Achieving this level often requires advanced degrees, certifications, and a strong track record of impactful projects.

Is 30 too late for data science?

Data Science Engineers can enter the field at any age, including 30, as success depends on skills, experience, and continuous learning. Many professionals transition into data science later in their careers by acquiring relevant knowledge in programming, statistics, and tools like Python or R. Age is less important than demonstrated expertise and the ability to adapt to evolving technologies.

What are the key skills and qualifications needed to thrive in the Data Science Engineer position, and why are they important?

A Data Science Engineer should have a strong background in statistics, machine learning, programming (typically Python or R), and data engineering, often supported by a degree in computer science, engineering, or a related field. Familiarity with data processing frameworks (like Spark or Hadoop), cloud platforms (AWS, GCP, or Azure), and certifications in data science or cloud technology are highly valued. Excellent problem-solving skills, communication abilities, and collaboration are essential soft skills for working effectively in cross-functional teams. These competencies enable Data Science Engineers to build scalable data solutions, deliver actionable insights, and drive business impact.

What are the typical daily responsibilities of a Data Science Engineer?

Data Science Engineers typically spend their days designing and building data pipelines, preparing and cleaning large datasets, and developing machine learning models to solve business problems. They work closely with data scientists, software engineers, and business stakeholders to translate requirements into scalable technical solutions. Responsibilities also include deploying models to production, monitoring their performance, and iterating on solutions based on feedback. This role offers a dynamic mix of coding, data analysis, and teamwork, making each day varied and intellectually engaging.

What is a Data Science Engineer job?

A Data Science Engineer is a professional who bridges the gap between data science and software engineering. They focus on designing, building, and maintaining scalable data pipelines, infrastructure, and machine learning models for production use. Their role involves data preprocessing, model deployment, performance optimization, and integrating AI solutions into applications. They work closely with data scientists, software engineers, and DevOps teams to ensure efficient data workflows.

What does a data science engineer do?

A data science engineer designs, develops, and maintains data pipelines and infrastructure to support data analysis and machine learning models. They work with large datasets, use programming languages like Python or Scala, and often collaborate with data scientists and software engineers to ensure data quality and accessibility.

Is data science high paying?

Data science engineers typically earn high salaries due to their specialized skills in statistical analysis, programming, and machine learning. Salaries vary by experience, location, and industry, but data science roles are generally considered well-compensated within the tech field.
What job categories do people searching Data Science Engineer jobs in California look for? The top searched job categories for Data Science Engineer jobs in California are:
What cities in California are hiring for Data Science Engineer jobs? Cities in California with the most Data Science Engineer job openings:
Infographic showing various Data Science Engineer job openings in California as of July 2026, with employment types broken down into 1% As Needed, 83% Full Time, 12% Part Time, 1% Temporary, and 3% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $128,018 per year, or $61.5 per hour.
Staff Data Science Engineer - Hardware & Silicon Validation

Staff Data Science Engineer - Hardware & Silicon Validation

Marvell Technology, Inc.

Santa Clara, CA โ€ข On-site

Full-time

Life, Retirement

Re-posted 7 days ago


Job description

About Marvell
Marvell's semiconductor solutions are the essential building blocks of the data infrastructure that connects our world. Across enterprise, cloud and AI, and carrier architectures, our innovative technology is enabling new possibilities.
At Marvell, you can affect the arc of individual lives, lift the trajectory of entire industries, and fuel the transformative potential of tomorrow. For those looking to make their mark on purposeful and enduring innovation, above and beyond fleeting trends, Marvell is a place to thrive, learn, and lead.
Your Team, Your Impact
The existing and upcoming megatrends of cloud services, video streaming, 5G wireless and AI/ML among others, are driving the relentless demand for higher bandwidth, lower power and smaller footprint. Marvell offers a field proven solution for high-speed optical interconnects and transceivers that are utilized for a wide array of enterprise, carrier, small medium business, industrial and cloud data center applications.
What You Can Expect
Key Responsibilities
Build Data Pipelines:
Design and develop scalable data pipelines to ingest, process, and store large volumes of DSP validation and test data
Data Analysis & Modeling:
Apply statistical analysis and machine learning techniques to identify patterns, detect anomalies, and support root-cause analysis
Visualization & Dashboarding:
Develop intuitive dashboards and visualizations to enable AE/FAE and validation engineers to quickly interpret test results and debug issues
Cloud-Based Analytics:
Leverage cloud technologies to process and analyze large-scale datasets efficiently, enabling near real-time insights
Collaboration with Engineering Teams:
Work closely with hardware, firmware, and validation engineers to understand data, define metrics, and translate complex data into actionable insights Automation & Efficiency:
Build tools and workflows that reduce manual debugging effort and accelerate validation cycles
What Makes This Role Exciting
  • Work on cutting-edge high-speed connectivity systems (DSP/PHY)
  • Apply AI/ML to real-world hardware validation challenges
  • Build end-to-end data platforms (from ingestion โ†’ analytics โ†’ visualization)
  • Direct impact on product quality and time-to-market
  • Opportunity to contribute to next-generation AI-driven debugging platforms

What We're Looking For
We are seeking a highly motivated Data Scientist / Data Analyst to support data analysis and data mining for high-speed DSP (Digital Signal Processing) validation and interoperability testing. This role focuses on building scalable data pipelines, developing intelligent analytics, and delivering actionable insights to accelerate debug and validation cycles.
You will work at the intersection of hardware systems, large-scale data, and AI-driven analytics, enabling engineers to quickly identify issues, optimize system performance, and improve product quality.
Minimum Qualifications
  • Bachelor's degree in Computer Science, Electrical Engineering, or related field with 3-5 years of industry experience, or Master's / PhD with 1-2 years of experience
  • Strong foundation in data analysis, statistical modeling, and machine learning
  • Proficiency in Python (pandas, numpy, matplotlib/seaborn, scikit-learn or similar)
  • Experience with data visualization tools such as Tableau or equivalent (e.g., Power BI, Superset)
  • Experience working with large datasets and performing data cleaning, transformation, and feature engineering

Preferred Qualifications
  • Experience with cloud platforms (e.g., Amazon Web Services, Snowflake, Databricks)
  • Familiarity with data pipeline development (ETL, streaming, batch processing)
  • Experience with time-series data analysis or signal/data from hardware systems
  • Exposure to DSP systems, networking, or semiconductor validation workflows
  • Experience with SQL and database systems (e.g., Snowflake, PostgreSQL)
  • Knowledge of machine learning for anomaly detection, prediction, or optimization
  • Familiarity with dashboard design for engineering workflows

#LI-TM1
Expected Base Pay Range (USD)
108,220 - 162,100, $ per annum
The successful candidate's starting base pay will be determined based on job-related skills, experience, qualifications, work location and market conditions. The expected base pay range for this role may be modified based on market conditions.
Additional Compensation and Benefit Elements
Marvell is committed to providing exceptional, comprehensive benefits that support our employees at every stage - from internship to retirement and through life's most important moments. Our offerings are built around four key pillars: financial well-being, family support, mental and physical health, and recognition. Highlights include an employee stock purchase plan with a 2-year look back, family support programs to help balance work and home life, robust mental health resources to prioritize emotional well-being, and a recognition and service awards to celebrate contributions and milestones. We look forward to sharing more with you during the interview process.
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability or protected veteran status.
Any applicant who requires a reasonable accommodation during the selection process should contact Marvell HR Helpdesk at TAOps@marvell.com.
Interview Integrity
To support fair and authentic hiring practices, candidates are not permitted to use AI tools (such as transcription apps, real-time answer generators like ChatGPT or Copilot, or automated note-taking bots) during interviews.
These tools must not be used to record, assist with, or enhance responses in any way. Our interviews are designed to evaluate your individual experience, thought process, and communication skills in real time. Use of AI tools without prior instruction from the interviewer will result in disqualification from the hiring process.
This position may require access to technology and/or software subject to U.S. export control laws and regulations, including the Export Administration Regulations (EAR). As such, applicants must be eligible to access export-controlled information as defined under applicable law. Marvell may be required to obtain export licensing approval from the U.S. Department of Commerce and/or the U.S. Department of State. Except for U.S. citizens, lawful permanent residents, or protected individuals as defined by 8 U.S.C. 1324b(a)(3), all applicants may be subject to an export license review process prior to employment.
#LI-SA1