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Remote Data Science Jobs in California (NOW HIRING)

Corvel

Data Science Manager

Irvine, CA · On-site +1

The Manager Data Science is responsible for architecting, building, and deploying production-grade ... This position is open to remote or hybrid. ESSENTIAL FUNCTIONS & RESPONSIBILITIES: * Design, build ...

Jj

Manager, Data Science - Oncology

San Diego, CA · On-site +1

Data Science Job Category: People Leader All Job Posting Locations: Cambridge, Massachusetts ... Proven record leading improvement initiatives with multi-disciplinary and remote partners.

Fullpower Technologies

Data Scientist

Santa Cruz, CA · Remote

$130K - $170K/yr

Fullpower-AI delivers a complete B2B IoT platform for AI-powered algorithms, remote contactless ... The ideal candidate will have a strong background in machine learning and data science and a proven ...

Mercor

Chief Data Scientist

San Francisco, CA · Remote

$70 - $100/hr

Data Science Experts Type: Contract Compensation: $70-$100/hour Location: Remote Commitment: 40 hours/week Role Responsibilities * Guide research and engineering teams on data science methodology ...

Tanisha Systems, Inc.

Data Scientist

Santa Clara, CA · On-site +1

Data Scientist Salary - Market (DOE) REMOTE / Work From Home Full-Time / Direct-Hire As Data Scientist, you will spearhead the end-to-end development of sales forecasting and demand sensing models ...

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Remote Data Science information

What are the key skills and qualifications needed to thrive as a Remote Data Scientist, and why are they important?

To thrive as a Remote Data Scientist, you need strong analytical skills, proficiency in statistics, and a solid background in mathematics or computer science, often supported by a relevant degree. Expertise in programming languages such as Python or R, familiarity with machine learning libraries, and experience with cloud-based data platforms are typically required. Excellent communication, self-motivation, and time management skills help you effectively collaborate and deliver results in a remote environment. These skills ensure accurate data analysis, meaningful insights, and successful teamwork despite physical distance.

How do remote data scientists typically collaborate with cross-functional teams to deliver insights?

Remote data scientists often work closely with product managers, engineers, and business analysts using digital collaboration tools such as Slack, Zoom, and project management platforms. Regular virtual meetings, code sharing via Git repositories, and clear documentation are essential to ensure alignment and transparency. While working remotely can present challenges in communication, proactive updates and scheduled syncs help foster strong teamwork and keep projects on track.

What is remote data science?

Remote data science refers to the practice of performing data analysis, modeling, and interpretation tasks from a location outside of a traditional office, such as from home or a co-working space. Remote data scientists use tools like Python, R, and SQL to analyze data, build predictive models, and communicate insights to stakeholders, all while collaborating virtually with their teams. This setup offers flexibility and can increase access to global job opportunities, but also requires strong self-motivation and communication skills to be effective.

Can a data scientist work fully remote?

Yes, many data scientists work fully remote, especially in companies that prioritize flexible work arrangements. Remote data science roles often require strong communication skills, proficiency with collaboration tools, and the ability to work independently on projects using programming languages like Python or R. However, some positions may require occasional in-person meetings or on-site presence depending on company policies.

Is 40 too late for data science?

Age is not a barrier to entering data science, and many professionals start or transition into the field later in life. Success depends on acquiring relevant skills such as programming, statistics, and machine learning, often through online courses or certifications, regardless of age.

Will AI replace data scientists?

AI is transforming the role of data scientists by automating routine tasks such as data cleaning and basic analysis, but it does not eliminate the need for human expertise in interpreting results, designing models, and making strategic decisions. Data scientists will continue to be essential for developing complex algorithms, understanding business context, and ensuring ethical use of AI tools. Skills in programming, statistical analysis, and machine learning remain critical for the profession's evolving landscape.

What Are the Qualifications to Get a Remote Data Science Job?

The qualifications for a remote data scientist depend in large part on your employer and their industry. Most employers expect remote data science professionals to have at least a bachelor’s degree in statistics, math, computer science, or a related field. Some expect postgraduate degrees in a field like data mining or machine learning or demonstrable skills in these areas. As a remote worker, you need access to relevant programs and an internet connection. You may also want to pursue certification, such as becoming a Certified Analytics Professional (CAP).

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

AspectRemote Data ScienceRemote Data Analyst
Required CredentialsDegree in Data Science, Statistics, or related field; programming skills in Python/R; knowledge of machine learningDegree in Statistics, Mathematics, or related field; proficiency in Excel, SQL, and data visualization tools
Work EnvironmentCollaborative teams, research-focused, often involves building models and algorithmsData reporting, visualization, and interpreting data trends for decision-making
Employer & Industry UsageTech companies, finance, healthcare, e-commerceMarketing agencies, retail, finance, healthcare

Remote Data Science involves developing predictive models and advanced analytics, requiring programming and machine learning skills. Remote Data Analysts focus on interpreting data, creating reports, and visualizations. While both roles analyze data remotely, Data Scientists typically handle more complex modeling tasks, whereas Data Analysts focus on data interpretation and reporting.

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 results come from 20% of the efforts or features. Data scientists often use this concept to focus on the most impactful variables, optimize models, and prioritize tasks for efficiency.
More about Remote Data Science jobs
What are the most commonly searched types of Data Science jobs in California? The most popular types of Data Science jobs in California are:
What are popular job titles related to Remote Data Science jobs in California? For Remote Data Science jobs in California, the most frequently searched job titles are:
What job categories do people searching Remote Data Science jobs in California look for? The top searched job categories for Remote Data Science jobs in California are:
What cities in California are hiring for Remote Data Science jobs? Cities in California with the most Remote Data Science job openings:
Remote Data Science jobs near you
Senior Data Scientist *** Direct End Client ***

Senior Data Scientist *** Direct End Client ***

Projas Technologies, LLC

San Diego, CA • On-site, Remote

Other

Posted 14 days ago

Job description

Job Title: Senior Data Scientist Product & Marketing Analytics

Job Description:
Do you thrive on turning complex data into actionable insights? Are you passionate about influencing product, marketing, and business strategy through data? We re looking for a Senior Data Scientist to join a high-impact team focused on driving top-of-funnel growth for a suite of digital service offerings.

This role sits at the intersection of data science, product strategy, marketing analytics, and customer insights. You will work cross-functionally with Product, Marketing, Sales, Customer Success, Engineering, and Finance teams to uncover new opportunities and implement scalable data-driven solutions that accelerate business growth.

Key Responsibilities:

  • Collaborative Problem Solving: Work closely with stakeholders to identify business problems, formulate hypotheses, and scope data science projects with measurable ROI.

  • Analytics & Business Insights: Analyze large-scale datasets to extract insights that influence product development, marketing campaigns, customer segmentation, and strategic planning.

  • Experimentation & Testing: Design and evaluate A/B tests and multivariate experiments to measure the impact of new features and marketing initiatives.

  • Predictive Modeling: Develop and deploy machine learning models and statistical algorithms to support personalization, targeting, and optimization strategies.

  • Data Visualization & Storytelling: Create intuitive dashboards and reports to track performance metrics and effectively communicate findings to technical and non-technical stakeholders.

  • Data Infrastructure & Quality: Partner with data engineering teams to improve data pipelines, define tracking requirements, and ensure high data integrity.

  • Best Practices & Standards: Promote reproducibility, rigorous testing, and high code quality across the data science organization.

Qualifications:

  • 5 7 years of experience in data science, analytics, or quantitative roles within tech, fintech, SaaS, or digital marketing domains.

  • Proficient in SQL and Python with experience using libraries such as pandas, scikit-learn, statsmodels.

  • Strong statistical background, including A/B testing, causal inference, regression, and uplift modeling.

  • Experience with marketing analytics, funnel optimization, or growth experimentation is a plus.

  • Skilled in data visualization using Tableau, Looker, or similar tools.

  • Proven ability to communicate technical results to executive and non-technical audiences.

  • Bachelor's degree in a quantitative field required; Master's or Ph.D. in Data Science, Statistics, Computer Science, Economics, or related discipline preferred.

  • Comfortable working in cross-functional, matrixed environments.


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