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Entry Level Urban Data Science Jobs in California

Associate Data Scientist

Burbank, CA · On-site

$62K - $63K/yr

We are seeking an Associate Data Scientist for this entry-level role. You will work to support the team in building ML-powered analyses and products that shape business strategy, optimize content ...

Associate Data Scientist

Burbank, CA

$62K - $63K/yr

We are seeking an Associate Data Scientist for this entry-level role. You will work to support the team in building ML-powered analyses and products that shape business strategy, optimize content ...

data (Entry Level)

San Francisco, CA · On-site

$20 - $26.75/hr

From staffing to full implementation of projects we provide the highest quality IT Services. We Focus on Java/Full stack and Data Science/Machine learning/Python/AI candidates. You'll be responsible ...

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Entry Level Urban Data Science information

What is an Entry Level Urban Data Scientist?

An Entry Level Urban Data Scientist is a professional who uses data analysis and computational techniques to help understand and improve urban environments. They work with data related to city infrastructure, transportation, population, and other urban systems to identify trends, solve problems, and support decision-making for city planning and management. This role typically involves tasks such as data cleaning, visualization, and basic modeling, often under the guidance of more experienced data scientists or urban planners. It's a great starting point for those interested in the intersection of data science and urban studies.

What types of projects do entry-level urban data scientists typically work on, and how do they collaborate with other city planning professionals?

As an entry-level urban data scientist, you can expect to work on projects such as analyzing traffic patterns, evaluating public transit usage, or assessing the impact of zoning changes using large datasets. You will often collaborate closely with urban planners, GIS specialists, and policy analysts to translate data insights into actionable recommendations for city development. This role usually involves regular team meetings to align on project goals, sharing findings through reports or presentations, and occasionally participating in community engagement efforts to ensure data-driven decisions reflect public needs.

What are the key skills and qualifications needed to thrive as an Entry Level Urban Data Scientist, and why are they important?

To thrive as an Entry Level Urban Data Scientist, you generally need a solid background in statistics, data analysis, and urban studies, often supported by a relevant degree such as data science, urban planning, or geography. Familiarity with programming languages like Python or R, GIS software (e.g., ArcGIS, QGIS), and data visualization tools is typically required. Strong problem-solving abilities, effective communication, and teamwork are crucial soft skills for translating data insights into actionable urban solutions. These skills and qualities are important because they enable you to analyze complex city data and contribute to evidence-based urban planning decisions.

What is the 80 20 rule in data science?

In data science, including entry-level urban data science roles, the 80/20 rule—also known as the Pareto principle—suggests that roughly 80% of results come from 20% of the efforts or data. This concept helps data scientists focus on the most impactful features, variables, or tasks to optimize analysis and model performance.

What is the difference between Entry Level Urban Data Science vs Entry Level Geographic Information Systems (GIS) Analyst?

AspectEntry Level Urban Data ScienceEntry Level Geographic Information Systems (GIS) Analyst
Required CredentialsBachelor's in Data Science, Urban Planning, or related field; basic programming skillsBachelor's in Geography, GIS, or related field; GIS certifications often preferred
Work EnvironmentUrban planning agencies, transportation departments, tech firmsGovernment agencies, environmental firms, urban planning departments
Industry UsageData analysis, modeling, urban analyticsMapping, spatial data management, GIS software applications
Common Search & ComparisonYesYes

Entry Level Urban Data Science focuses on analyzing urban data using programming and statistical tools, often involving urban analytics and modeling. In contrast, Entry Level GIS Analysts primarily work with spatial data and mapping software to create geographic visualizations. Both roles are common in urban planning and government sectors, but they emphasize different skill sets and tools.

Is it possible to get a data science job with no experience?

Entry level urban data science positions often require some foundational skills in programming, statistics, and data analysis, but many employers consider candidates with relevant coursework, certifications, or internships. Building a portfolio of projects using tools like Python, R, or SQL can improve chances, and entry-level roles typically do not demand extensive professional experience.

How do I become a data scientist with no experience?

To become an entry level urban data scientist with no experience, focus on building foundational skills in programming (such as Python or R), statistics, and data visualization. Gaining hands-on experience through online courses, personal projects, and internships, along with learning tools like SQL and GIS software, can help demonstrate your capabilities to employers.

Is 30 too late for data science?

Entry Level Urban Data Science roles are accessible to individuals of various ages, including those starting a career at 30 or later. Success depends on acquiring relevant skills such as programming, statistics, and data analysis, often through online courses or certifications, and building a strong portfolio. Age is generally not a barrier if you demonstrate proficiency and a commitment to learning in the field.
What job categories do people searching Entry Level Urban Data Science jobs in California look for? The top searched job categories for Entry Level Urban Data Science jobs in California are:
Infographic showing various Entry Level Urban Data Science 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 88% Physical, 3% Hybrid, and 9% Remote job distribution.
Sustainment Data Science & Analysis Support

Sustainment Data Science & Analysis Support

JSL Technologies Incorporated

San Diego, CA

$65K - $75K/yr

Other

Posted 11 days ago


Job description

Description

Sustainment Data Science & Analysis Support

San Diego, CA

About Us:


JSL Technologies, Inc. (JSL) is a certified Small Disadvantaged Business (SDB) and Veteran-Owned government contractor delivering engineering, logistics, and program support services to the Department of Defense (DoD). Our team of more than 200 professionals is dedicated to providing practical, innovative, and cost-effective solutions that support critical missions.

Headquartered in Oxnard, California, JSL supports government customers across the nation. We foster a culture built on integrity, collaboration, and accountability, empowering our employees to perform at a high level and continuously improve the way we serve our customers.

At JSL, our people are the foundation of our success. We offer competitive compensation and a comprehensive benefits package that supports the well-being and professional growth of our team.


Job Description:


JSL Technologies is seeking an entry-level Data Scientist to support Navy engineering and sustainment programs through Python-based data analysis, automation, visualization, and analytical tool development. This position is well suited for a recent college graduate with strong Python programming skills and an interest in applying data science techniques to equipment reliability, maintenance, readiness, and sustainment challenges. Prior Navy, Reliability, Availability, Maintainability, and Cost (RAM-C), logistics, or sustainment experience is not required. The selected candidate will receive exposure to Navy systems, data sources, analytical processes, and reliability terminology while working with experienced engineering and logistics personnel at the Government facility in San Diego, California.

Develop, maintain, and improve Python scripts used to collect, clean, organize, validate, and analyze engineering, maintenance, logistics, readiness, and operational data.

Work with structured and unstructured datasets to identify trends, patterns, anomalies, and data-quality issues.

Automate repetitive data-processing, reporting, and visualization activities using Python and related analytical libraries.

Support the development of dashboards, charts, reports, and other data products used by engineering and program stakeholders.

Apply statistical analysis, machine learning, predictive analytics, or other data-science methods under the guidance of senior technical personnel.

Assist experienced engineers and analysts in evaluating reliability, availability, maintainability, cost, readiness, and sustainment data.

Support senior engineers and analysts in developing and evaluating RAM-C and supportability products, including reliability models, failure analyses, repair-level analyses, sparing analyses, and readiness metrics. Prior experience with these products is not required.

Support the preparation of technical reports, readiness summaries, recurring status reports, and presentation materials.

Document data sources, analytical methods, assumptions, code, and results so analyses are understandable and repeatable.

Collaborate with engineers, logisticians, maintenance personnel, program personnel, and other stakeholders to understand analytical requirements.

Learn and use Government-provided systems and tools, which may include Advana Jupiter, JIRA, Tableau, and Navy maintenance or readiness databases.

Become familiar with RAM-C concepts and analytical products such as Failure Modes, Effects and Criticality Analysis (FMECA), Level of Repair Analysis (LORA), Mean Time Between Failures (MTBF), Mean Time to Repair (MTTR), Mean Logistics Delay Time (MLDT), and sparing analysis through on-the-job training.

Participate in technical meetings and design reviews and provide data-analysis support as assigned.

Requirements

  .  

Minimum Qualifications:


Must be legally authorized to work in the United States without the need for employer sponsorship now or at any time in the future.

Ability to obtain and maintain a U.S. Government Secret security clearance.

Bachelor's degree in Data Science, Computer Science, Software Engineering, Computer Engineering, Mathematics, Statistics, Operations Research, Engineering, or a closely related technical discipline.

Academic, internship, research, project, or professional experience developing software or performing data analysis using Python.

Strong understanding of Python programming fundamentals, including data structures, functions, object-oriented programming, debugging, and code documentation.

Experience using common Python data-analysis libraries such as pandas, NumPy, SciPy, scikit-learn, Matplotlib, or comparable libraries.

Ability to clean, transform, analyze, and visualize data from multiple sources.

Basic understanding of statistics, data modeling, machine learning, or predictive analytics.

Ability to communicate analytical results clearly through written reports, visualizations, and presentations.

Ability to learn unfamiliar engineering, reliability, logistics, and Navy terminology.

Ability to work collaboratively with engineers, analysts, logisticians, and Government personnel.

Ability to work on-site at the Government facility in San Diego, CA.


Preferred Qualifications:


Internship, academic, research, or project experience involving Python-based data analysis, automation, machine learning, or predictive modeling.

Experience working with large, incomplete, or inconsistent datasets.

Experience using Git or another version-control platform.

Experience with SQL, relational databases, APIs, cloud-based analytics platforms, or data visualization tools.

Experience with Tableau, Power BI, or comparable visualization tools.

Coursework or project experience related to reliability engineering, maintenance analytics, operations research, logistics, or equipment sustainment.

Familiarity with Department of Defense or Navy programs is beneficial but not required.

Familiarity with JIRA, Advana Jupiter, FMECA, LORA, MTBF, MTTR, MLDT, or availability analysis is beneficial but not required.


Security Clearance:


Applicants must have an active security clearance and/or the ability to obtain and maintain a US Government Security Clearance. Selected candidates will be subject to a government security investigation and must meet eligibility requirements to obtain a DoD Government-granted security clearance. Individuals will be subject to a background investigation to include but not limited to, criminal history, employment and education verification, drug testing, and creditworthiness.


EEO:


JSL Technologies, Inc. is an equal opportunity employer. We provide equal employment opportunities to all qualified applicants and employees without discrimination with regard to race, religion, creed, color, sex, sex stereotype, pregnancy, childbirth or related medical conditions, age, sexual orientation, gender, gender identification and expression, transgender status, transitioning employees, physical or mental disability, medical condition, genetic characteristics, genetic information, marital status, registered domestic partner status, status as military, or as a veteran or as a qualified disabled veteran, ancestry, citizenship, national origin.

To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed must be representative of the knowledge, skills, minimum education, training, licensure, experience, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform essential functions. Please contact HR@jsltechinc.com if you need accommodation for the application process.