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Entry Level Scientific Computing Jobs in California

Beta Soft System Inc.

QA Tester/ Analyst

Fremont, CA

A recent degree in Computer Science, Engineering, or Electrical Engineering. * Eligibility and ... JOB Title - Beta Soft Systems is looking to hire Entry level QA Engineers. ( ) Beta Soft Systems is ...

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Entry Level Scientific Computing information

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How much do entry level scientific computing jobs pay per hour?

As of Aug 5, 2026, the average hourly pay for entry level scientific computing in California is $17.24, according to ZipRecruiter salary data. Most workers in this role earn between $15.43 and $18.75 per hour, depending on experience, location, and employer.

What are some common challenges faced by entry level scientific computing professionals, and how can they be overcome?

Entry-level professionals in scientific computing often encounter challenges such as adapting to complex codebases, learning new programming languages or tools, and understanding interdisciplinary project requirements. Collaborating closely with senior team members, actively seeking feedback, and dedicating time to continuous learning can help overcome these hurdles. Additionally, participating in code reviews and leveraging online resources or documentation can accelerate skill development and integration into the team.

What are the key skills and qualifications needed to thrive as an entry level scientific computing professional?

To thrive as an Entry Level Scientific Computing professional, you need a solid grounding in mathematics, programming (often Python, C++, or MATLAB), and scientific principles, typically supported by a degree in computer science, physics, engineering, or a related field. Familiarity with scientific computing libraries (such as NumPy, SciPy), version control systems (like Git), and computational frameworks is commonly required. Strong problem-solving skills, attention to detail, and effective communication help individuals excel when collaborating with multidisciplinary teams. These skills ensure accurate data analysis, reproducible research, and efficient solutions to complex scientific problems.

What is the difference between Entry Level Scientific Computing vs Data Analyst?

AspectEntry Level Scientific ComputingData Analyst
Required CredentialsBachelor's in Science, Engineering, or related field; basic programming skillsBachelor's in Statistics, Mathematics, or related field; proficiency in data tools
Work EnvironmentResearch labs, scientific institutions, tech companiesBusiness, finance, healthcare sectors, often office-based
Employer & Industry UsageResearch projects, scientific simulations, modelingData interpretation, reporting, business insights

Entry Level Scientific Computing roles focus on applying programming and scientific methods to research and modeling tasks, often within research or tech environments. Data Analysts primarily interpret data to inform business decisions, working across various industries. While both roles require analytical skills and some programming knowledge, their focus and work settings differ significantly.

What is an entry level scientific computing job?

Entry level scientific computing jobs are positions for individuals who are beginning their careers in applying computational techniques to solve scientific problems. These roles often involve programming, data analysis, simulation, and modeling in fields like biology, physics, chemistry, or engineering. Typical responsibilities may include writing code to process data, running simulations, supporting research teams, and maintaining computational tools or software. Candidates usually have a background in science, mathematics, engineering, or computer science and are expected to have foundational knowledge of programming languages such as Python, R, or MATLAB. These positions are excellent starting points for gaining experience in computational research and development.
What are the most commonly searched types of Scientific Computing jobs in California? The most popular types of Scientific Computing jobs in California are:
What are popular job titles related to Entry Level Scientific Computing jobs in California? For Entry Level Scientific Computing jobs in California, the most frequently searched job titles are:
What job categories do people searching Entry Level Scientific Computing jobs in California look for? The top searched job categories for Entry Level Scientific Computing jobs in California are:
Entry Level Scientific Computing jobs near you
Infographic showing various Entry Level Scientific Computing job openings in California as of July 2026, with employment types broken down into 100% Full Time. Highlights an 74% In-person, and 26% Remote job distribution, with an average salary of $35,851 per year, or $17.2 per hour.

Biostatistics Scientist (Plant Science)

Sakata Seed America, INC.

Woodland, CA • On-site

$90K - $105K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 8 days ago

Job description


JOB SUMMARY

The Biostatistics Scientist supports applied statistical analysis, quantitative genetics, and breeding analytics for vegetable crop research programs. This entry-level role contributes to genomic, phenotypic, and field-trial data analysis under the guidance of senior scientists, managers and cross-functional project teams. The position helps develop reliable, reproducible analytical workflows that improve trait evaluation, marker-assisted selection, genomic prediction, and data-driven breeding decisions.

Key Responsibilities

Statistical Analysis, Quantitative Genetics & Genomic Prediction

  • Support the development, testing, and interpretation of statistical and genomic prediction models for vegetable crop breeding programs.
  • Apply standard statistical and quantitative genetics methods, such as mixed models, heritability estimation, genetic correlations, and basic genomic prediction approaches.
  • Assist with evaluating model performance, prediction accuracy, and data quality across populations, environments, and breeding stages.
  • Contribute to analyses that help breeders understand trait variation, experimental results, and selection opportunities.
  • Document methods, assumptions, code, and results clearly to support reproducibility and team review.

Molecular Marker & Trait Analytics

  • Analyze molecular marker datasets, including SNP and haplotype data, to support trait mapping, marker validation, and breeding decisions.
  • Assist molecular and breeding teams with data summaries for marker development, marker deployment, and trait evaluation projects.
  • Support quality control of genotypic and phenotypic datasets, including data cleaning, formatting, consistency checks, and basic exploratory analysis.
  • Help prepare selection metrics, trait summaries, and visualizations that integrate multiple sources of breeding data.
  • Translate analytical results into concise summaries that can be reviewed by breeders, molecular scientists, and project teams.

Genomic, Phenotypic & Field Trial Data Analysis

  • Prepare, manage, and analyze genomic, phenotypic, greenhouse, and field-trial datasets under guidance from senior team members.
  • Develop and maintain reproducible scripts for data quality control, statistical analysis, visualization, and reporting.
  • Contribute to the improvement of analytical templates, reporting workflows, and shared data practices in collaboration with bioinformatics and data teams.

Project Support & Cross-Functional Collaboration

  • Support analytical components of breeding, trait development, molecular marker, and technology projects.
  • Collaborate with breeders, phenotyping, molecular biology, bioinformatics, and data teams to understand project objectives and data requirements.
  • Prepare clear technical summaries, tables, figures, and presentations to communicate results to internal stakeholders.
  • Learn and apply current methods in biostatistics, quantitative genetics, breeding analytics, and reproducible scientific computing.


Required Qualifications

Education

  • PhD in Biostatistics, Statistics, Quantitative Genetics, Plant Breeding, Computational Biology, Data Science, or a related field; industry experience a plus
    or
  • MS in Biostatistics, Statistics, Quantitative Genetics, Plant Breeding, Computational Biology, Data Science, or a related field with 0–2 years of relevant academic, internship; industry experience a plus


Experience & Technical Skills

  • Foundational training in statistics, biostatistics, quantitative genetics, plant breeding, computational biology, or related analytical disciplines.
  • Experience with statistical analysis of biological, genomic, phenotypic, field-trial, or experimental datasets through graduate research, internships, or applied projects.
  • Working knowledge of statistical programming in R, Python, SAS, or similar tools.
  • Good understanding of experimental design, mixed models, regression, data visualization, and reproducible analytical workflows.
  • Experience with molecular markers, genomic data, plant breeding concepts, or trait analysis is desirable.
  • Ability to learn new methods, manage multiple analytical tasks, and deliver accurate results with guidance.
  • Strong attention to detail, scientific curiosity, communication skills, and willingness to collaborate across disciplines.

Preferred Qualifications

  • Research experience in plant breeding, seed industry research, agricultural biotechnology, or applied life-science data analysis.
  • Experience in genomic prediction, QTL mapping, GWAS, marker-assisted selection, or trait discovery workflows.
  • Familiarity with breeding databases, phenotyping systems, laboratory information systems, or integrated data platforms.
  • Experience preparing figures, tables, dashboards, or technical reports for scientific or cross-functional audiences.
  • Exposure to cloud-based, Linux, Git, or high-performance computing environments for data analysis.
  • Interest in applying AI, machine learning, and modern statistical methods to practical breeding and research questions.

Competencies & Behaviors

  • Demonstrates curiosity, initiative, and accountability in learning new analytical methods and scientific workflows.
  • Applies statistical methods carefully, with attention to data quality, assumptions, and reproducibility.
  • Works collaboratively with scientists from breeding, molecular biology, phenotyping, bioinformatics, and data teams.
  • Communicates analytical results clearly to both technical and non-technical audiences.
  • Manages assigned tasks effectively, asks timely questions, and follows through on deliverables.
  • Contributes to a culture of scientific rigor, continuous improvement, teamwork, and practical problem solving.

Reporting Structure

  • Reports to Senior Biotech Manager

Works under the guidance of senior scientists, project leads, and cross-functional research teams

BENEFITS:

Health & Wellness
Medical, Dental & Vision Insurance
Monthly Wellness Stipend
Employee Assistance Program (EAP)

Employee Philanthropic Giving Program

Disability Insurance (plans vary by location)


Financial Benefits
401(k) Program + Company Match
Profit Sharing Program (via 401(k)

Holiday Bonus

Performance Incentive Bonus Program
Tuition Reimbursement

529 College‑Savings Plan
Company-Paid Basic Life & AD&D Insurance


Time Off & Flexibility
Paid Vacation
Paid Sick Leave
15 Paid Company Holidays
2 Floating Holidays

Birthday Off