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Data Science Jobs in Encinitas, CA (NOW HIRING)

RXinsider LTD.

Manager Data Science

San Diego, CA · On-site

$142 - $236/hr

As part of the Data Science and Engineering team, this role combines strategic leadership, technical expertise, and people management to deliver innovative data solutions while ensuring robust ...

New

Dexcom, Inc.

Manager Data Science

San Diego, CA · On-site

As part of the Data Science and Engineering team, this role combines strategic leadership, technical expertise, and people management to deliver innovative data solutions while ensuring robust ...

Dexcom

Manager Data Science

San Diego, CA · On-site

You will lead and manageglobal,data scientists and engineers, ensuring high-quality, reliableaccess to data. * Youwilloversee the design, development, validation, and deployment of machine learning ...

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 ...

New

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 ...

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All JobsData Science JobsData Science Jobs in Encinitas, CA

Data Science information

See Encinitas, CA salary details

$40.3K

$131.8K

$211.1K

How much do data science jobs pay per year?

As of Aug 21, 2026, the average yearly pay for data science in Encinitas, CA is $131,832.00, according to ZipRecruiter salary data. Most workers in this role earn between $105,800.00 and $146,100.00 per year, depending on experience, location, and employer.

What is data science?

Data science is an interdisciplinary field that uses scientific methods, algorithms, and systems to extract insights and knowledge from structured and unstructured data. It combines skills from statistics, computer science, and domain expertise to analyze and interpret complex data sets. Data scientists work with large amounts of data to identify patterns, make predictions, and help organizations make data-driven decisions.

What does a data scientist do?

As a Data Scientist, you are qualified to work in such diverse fields as research and development, politics, advertising and marketing, technology, healthcare, government, and higher education as well as multiple others. In general, your duties and responsibilities will be to compile and analyze relevant statistics and turn those numbers into algorithms that reveal insights that can be used by other researchers in their areas of study. Data Science can reveal things like consumer buying habits or the likelihood of success for a course of action. Other duties might vary, depending on your unique field of specialty. Related areas in which a Data Scientist might wish to focus include work as a Data Analyst, Machine Learning Engineer, and Project Manager.

What are the key skills and qualifications needed to thrive as a data scientist, and why are they important?

To thrive as a Data Scientist, you need a strong background in statistics, programming (often Python or R), and data analysis, usually supported by a degree in a quantitative field. Familiarity with machine learning libraries (like scikit-learn or TensorFlow), big data tools (such as Hadoop or Spark), and data visualization platforms is typically required. Critical thinking, problem-solving, and effective communication are vital soft skills for translating complex data insights into actionable business strategies. These skills and qualities are essential for extracting value from data, driving informed decisions, and effectively collaborating with multidisciplinary teams.

What are some common challenges faced by data scientists when working with real-world datasets?

Data scientists often encounter challenges such as missing or inconsistent data, unstructured formats, and noisy information in real-world datasets. Cleaning and preprocessing data to ensure its quality can be time-consuming but is critical for building accurate models. Additionally, data scientists may work closely with domain experts and other team members to better understand the data's context and ensure their analyses align with business objectives. Overcoming these challenges requires strong problem-solving skills and effective collaboration within cross-functional teams.

What is the difference between Data Science vs Data Analyst?

AspectData ScienceData Analyst
Required skillsStatistics, programming (Python, R), machine learningData visualization, SQL, basic statistics
Work environmentDeveloping models, predictive analytics, researchReporting, data cleaning, descriptive analysis
Tools usedPython, R, Jupyter, TensorFlowExcel, SQL, Tableau, Power BI
Industry usageTech, finance, healthcare, e-commerceRetail, marketing, finance, healthcare

Data Science and Data Analyst roles often overlap but differ mainly in scope. Data Scientists focus on building predictive models and advanced analytics, requiring programming and machine learning skills. Data Analysts primarily handle data cleaning, reporting, and visualization. Both roles are essential in data-driven industries, but Data Science is more technical and research-oriented, while Data Analysis emphasizes interpreting data for business insights.

Is a data scientist in high demand?

Data scientists are in high demand across many industries due to the increasing reliance on data-driven decision making. The role requires skills in programming, statistics, and machine learning, and job growth is expected to continue as organizations expand their data capabilities.

What jobs can a data scientist do?

A data scientist can work in roles such as data analyst, machine learning engineer, data engineer, or business intelligence analyst. These roles involve analyzing large datasets, developing predictive models, and using tools like Python, R, and SQL to support decision-making across various industries.
More about Data Science jobs

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

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

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

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

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

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

Data Science jobs near you
Infographic showing various Data Science job openings in Encinitas, CA as of August 2026, with employment types broken down into 1% As Needed, 78% Full Time, 18% Part Time, and 3% Contract. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution, with an average salary of $131,832 per year, or $63.4 per hour.

Data Scientist, D2C Data Science

PlayStation Global

San Diego, CA

Full-time

Re-posted 18 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. 

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