ZipRecruiter

Log In

1

Weekend Data Science Jobs in Vista, CA (NOW HIRING)

Dlapiper

Data Scientist

San Diego, CA · On-site +1

Prioritizes, scopes and manages data science projects for internal stakeholders and clients. * Mines and analyzes data to drive optimization and improvement of product development, marketing ...

Waymo

Data Scientist

San Diego, CA

Autonomous driving presents a new paradigm in data science: in addition to leveraging data collected on-road, we generate our own data using state-of-the-art simulation technology-resulting in denser ...

Arenatechnologies

Data Scientist

San Diego, CA · On-site

$125 - $144/hr

Bachelor's Degree in Data Science or equivalent * 5 (Five) or more years of professional experience in data science related to training and development, with an artificial intelligence focus * Data ...

Qualcomm

Data Scientist, Staff

San Diego, CA · On-site

$142.10 - $213.10/hr

Company Qualcomm Incorporated Job Area Information Technology Group, Information Technology Group > Data Science General Summary As a leading technology innovator, Qualcomm pushes the boundaries of ...

next page

Showing results 1-20

All JobsData Science JobsWeekend Data Science JobsWeekend Data Science Jobs in Vista, CA

Weekend Data Science information

See Vista, CA salary details

$38.3K

$125.5K

$200.9K

How much do weekend data science jobs pay per year?

As of Aug 26, 2026, the average yearly pay for weekend data science in Vista, CA is $125,464.00, according to ZipRecruiter salary data. Most workers in this role earn between $100,700.00 and $139,000.00 per year, depending on experience, location, and employer.

What is a weekend data science?

A Weekend Data Science job typically refers to a part-time or contract-based data science position where the primary work hours are on weekends. These roles are ideal for students, professionals seeking extra income, or those looking to gain experience in the data science field without committing to a full-time weekday schedule. Weekend data scientists analyze data, build models, and generate insights just like full-time data scientists but with flexible or reduced hours that fit around a weekend schedule.

What skills and qualifications are needed to thrive as a weekend data scientist?

To thrive as a Weekend Data Scientist, you need strong analytical skills, proficiency in statistics, and expertise in programming languages such as Python or R, often supported by a degree in a quantitative field. Familiarity with data analysis tools like SQL, machine learning libraries (e.g., scikit-learn, TensorFlow), and data visualization platforms (e.g., Tableau) is typically required. Excellent time management, problem-solving ability, and effective communication are crucial soft skills for delivering insights on tight weekend deadlines. These skills ensure that data-driven decisions can be made efficiently and accurately, even within limited time frames.

What challenges do data scientists working on weekends face, and how can they be managed?

Data scientists working weekend shifts often encounter challenges such as limited access to colleagues for collaboration or support, since many team members may not be available outside standard business hours. Additionally, urgent issues or data anomalies may require quick, independent problem-solving. Proactive communication with weekday teams, thorough documentation, and setting up clear protocols for handoffs can help manage these challenges and ensure smooth workflow continuity.

What is the difference between Weekend Data Science vs Part-Time Data Analyst?

AspectWeekend Data SciencePart-Time Data Analyst
CredentialsTypically requires a degree in data science, statistics, or related fieldOften requires a degree or relevant experience in data analysis or related fields
Work EnvironmentProject-based, flexible hours, often remote or on-site during weekendsFlexible hours, may be remote or on-site, often with less technical complexity
Industry UsageUsed in tech, finance, healthcare, and startups for specialized projectsCommon in retail, marketing, and small businesses for routine data tasks

Weekend Data Science roles focus on complex data projects requiring advanced skills, often during weekends, while Part-Time Data Analysts handle routine data tasks with less technical depth, offering flexible schedules. Both roles serve different needs but share a focus on data work outside standard hours.

Do weekend data scientists work on weekends?

Weekend data scientists may work on weekends depending on project deadlines, company policies, or client needs. Typically, data science roles involve regular weekday hours, but some positions require weekend work, especially in roles with flexible or project-based schedules. It is important to clarify work hours during the hiring process or in job descriptions.

What are the most commonly searched types of Data Science jobs in Vista, CA?

The most popular types of Data Science jobs in Vista, CA are:

What are popular job titles related to Weekend Data Science jobs in Vista, CA?

For Weekend Data Science jobs in Vista, CA, the most frequently searched job titles are:

What job categories do people searching Weekend Data Science jobs in Vista, CA look for?

The top searched job categories for Weekend Data Science jobs in Vista, CA are:

What cities near Vista, CA are hiring for Weekend Data Science jobs?

Cities near Vista, CA with the most Weekend Data Science job openings:

Weekend Data Science jobs near you
Infographic showing various Weekend Data Science job openings in Vista, CA as of August 2026, with employment types broken down into 1% As Needed, 80% Full Time, 15% Part Time, and 4% Contract. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution, with an average salary of $125,464 per year, or $60.3 per hour.

Data Scientist, D2C Data Science

San Diego, CA

PlayStation Global
Manufacturing • 1 - 5K employees
Learn more about PlayStation Globalexternal

Full-time

Re-posted 22 days ago

Job description

Data Scientist, D2C Data Science 

San Diego, CA  (Hybrd)

About the Team 

The Direct to Consumer (D2C) Data Science organization brings together Data Science, Data Engineering, and ML Engineering to support PlayStation's digital business across commerce, payments, subscriptions, lifecycle experiences, and player-facing services. We partner closely with product, engineering, finance, marketing, operations, and data teams to turn experimentation, forecasting, modeling, and production-quality measurement into better decisions and better player experiences. 

About The Role 

We are looking for a Data Scientist to join a focused team within D2C Data Science supporting payment and subscription experiences across PlayStation's direct-to-consumer business. This is a hands-on role for someone who can use statistics, machine learning, experimentation, and strong data judgment to help teams make better decisions about how players pay, subscribe, and move through global payment flows. 

The initial portfolio is expected to focus on payment method performance, payment flow optimization, subscription payment recovery, and ROI-based evaluation of experiments and business interventions. You will help teams understand customer behavior, payment success, cost and routing tradeoffs, and the business impact of new payment capabilities. 

Our team values practical scientific rigor: clear decision framing, trusted reusable metrics, transparent uncertainty, and recommendations that help teams move faster without sacrificing measurement quality. This role is best suited for someone who can independently own well-scoped analyses and models, work through ambiguity, and translate complex data into recommendations that improve customer experience and business performance. 

Responsibilities 

  • Apply data science methods to high-impact questions across D2C payments, subscriptions, commerce, lifecycle, and player experience. 
  • Design, analyze, and interpret A/B tests, holdouts, quasi-experimental analyses, and other measurement approaches with clear hypotheses, metrics, and decision criteria. 
  • Analyze payment and subscription outcomes such as payment success, authorization performance, payment funnel behavior, routing or retry performance, cost tradeoffs, and subscription recovery. 
  • Build statistical and machine learning models for forecasting, segmentation, propensity, retention, payment success, payment optimization, subscription outcomes, or offer performance. 
  • Use SQL and Python to prepare data, validate assumptions, analyze behavior, and produce reproducible analytical workflows. 
  • Partner with product, engineering, finance, marketing, operations, and data engineering teams to ensure analyses are technically sound, actionable, and operationally useful. 
  • Communicate findings with clear recommendations, confidence levels, caveats, tradeoffs, next steps, and reusable documentation that supports better decision-making. 

Basic Qualifications 

  • 3+ years of professional experience in data science or machine learning 
  • Bachelor's degree in statistics, mathematics, computer science, engineering, data science, or a related quantitative field or equivalent  
  • Strong SQL and Python skills for data extraction, data validation, analysis, modeling, and reproducible workflows. 
  • Solid foundation in statistics, experimental design, machine learning, predictive modeling. 
  • Experience applying data science methods to ambiguous commercial, customer, payment, subscription, or operational problems. 
  • Ability to communicate technical findings clearly to technical and non-technical partners. 

Preferred Qualifications 

  • Experience with digital commerce, payments, billing, subscriptions, fintech, marketplaces, gaming, media, or scaled consumer technology businesses. 
  • Experience with payment method performance, authorization or success-rate analysis, payment optimization, routing or retry strategies, cost analysis, payment telemetry, or subscription recovery. 
  • Experience designing, running, or analyzing experiments, including A/B tests, holdouts, quasi-experimental approaches, or causal inference methods. 
  • Experience with forecasting, customer segmentation, churn / retention modeling, offer measurement, payment success modeling, subscription lifecycle analytics, or ROI-based business evaluation. 
  • Experience working with large-scale data environments such as Snowflake, Databricks, Spark, BigQuery, or similar platforms, and familiarity with metric layers or source-of-truth datasets. 

Apply