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Data Science Jobs in Encinitas, CA (NOW HIRING)

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 ...

Booz Allen Hamilton is a company focused on leveraging data science to solve global challenges. They are seeking a Mid Data Scientist to analyze complex data sets, develop algorithms, and provide ...

Stay current with emerging data science methodologies, tools, and industry best practices * Ensure compliance with data governance, privacy, and security standards Qualifications: * Education:

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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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 Jul 14, 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.

Is data science a good career?

Data science is a growing field with high demand for professionals skilled in statistics, programming, and data analysis tools like Python and R. It offers competitive salaries, diverse industry applications, and opportunities for advancement, making it a strong career choice for those with relevant skills and education.

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.

Is 40 too late for data science?

Data science is a field open to individuals of all ages, and many professionals transition into it later in their careers. Success often depends on acquiring relevant skills such as programming, statistics, and machine learning, which can be learned through online courses, bootcamps, or degrees regardless of age.

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 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 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.

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.

What work do you do as a Data Scientist?

A Data Scientist analyzes large datasets to extract insights, build predictive models, and inform business decisions. They use programming languages like Python or R, and tools such as SQL and machine learning frameworks, often working in collaborative environments with data engineers and analysts.

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 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:
Infographic showing various Data Science job openings in Encinitas, CA as of July 2026, with employment types broken down into 1% As Needed, 82% Full Time, 14% Part Time, and 3% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $131,832 per year, or $63.4 per hour.
Data Scientist, D2C Data Science

Data Scientist, D2C Data Science

PlayStation Global

San Diego, CA • On-site

Full-time

Medical, Dental, Vision, Retirement, PTO

Re-posted 10 days ago


Job description

Why Sony Interactive Entertainment?
Sony Interactive Entertainment isn't just the Best Place to Play - it's also the Best Place to Work. Sony Interactive Entertainment (SIE) is the company behind the PlayStation brand. As a subsidiary of Sony Group Corporation, we're part of a proud legacy of innovation and excellence. SIE is a dynamic technology company, delivering cutting-edge hardware and network services to more than 100 million people and an entertainment leader, home to some of the most beloved and recognizable intellectual properties (IP) in the world. Our role at SIE is to create and nurture the experiences under the PlayStation brand, a name synonymous with entertainment excellence and creativity.
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.

At SIE, we consider several factors when setting each role's base pay range, including the competitive benchmarking data for the market and geographic location.
Please note that the base pay range may vary in line with our hybrid working policy and individual base pay will be determined based on job-related factors which may include knowledge, skills, experience, and location.
In addition, this role is eligible for SIE's top-tier benefits package that includes medical, dental, vision, matching 401(k), paid time off, wellness program and coveted employee discounts for Sony products. This role also may be eligible for a bonus package. Click here to learn more.
The estimated base pay range for this role is listed below.
$143,400-$215,000 USD
Please note, Sony Interactive Entertainment conducts background checks at the offer stage for all new employees (which may include criminal background checks for some roles) and will need to process personal information to support these checks.
Please refer to our Candidate Privacy Notice for more information about what personal information we collect, how we use it, who we share it with, and your data protection rights.
Equal Opportunity Statement:
Sony is an Equal Opportunity Employer. All persons will receive consideration for employment without regard to gender (including gender identity, gender expression and gender reassignment), race (including colour, nationality, ethnic or national origin), religion or belief, marital or civil partnership status, disability, age, sexual orientation, pregnancy, maternity or parental status, trade union membership or membership in any other legally protected category.
We strive to create an inclusive environment, empower employees and embrace diversity. We encourage everyone to respond.
Sony Interactive Entertainment is a Fair Chance employer and qualified applicants with arrest and conviction records will be considered for employment.