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Internship Python R Jobs in Sacramento, CA (NOW HIRING)

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Internship Python R information

What is an internship Python R?

Internship Python R positions are entry-level opportunities for students or recent graduates to gain practical experience working with Python and R programming languages. These internships typically involve tasks such as data analysis, scripting, software development, or research projects that utilize Python and R for statistical computing and data manipulation. Interns learn valuable skills in coding, problem-solving, and collaborating on real-world projects, often in fields like data science, finance, healthcare, or academia. They provide a stepping stone for building a career in technology or data-driven industries.

What types of projects or tasks can I expect to work on during a Python/R internship?

As a Python/R intern, you’ll typically assist with data analysis, data cleaning, and visualization projects, often working alongside experienced data scientists or analysts. Many internships involve using Python or R to automate data processing, support research efforts, or build basic machine learning models. You may also contribute to documentation, testing code, and presenting findings to your team. This hands-on experience is designed to strengthen your programming skills and expose you to collaborative workflows common in data-driven environments.

What are the key skills and qualifications needed to thrive as an internship Python R, and why are they important?

To thrive in a Python/R internship, you need foundational knowledge in programming with Python and R, basic statistics, and a relevant academic background in computer science, data science, or a related field. Familiarity with data analysis libraries (like pandas, NumPy, tidyverse), version control systems (such as Git), and data visualization tools is typically required. Strong problem-solving abilities, attention to detail, and the willingness to learn make candidates stand out. These skills are crucial for effectively contributing to data-driven projects and adapting to the learning-focused environment of an internship.

What is the difference between Internship Python R vs Data Analyst?

AspectInternship Python RData Analyst
Required SkillsBasic Python and R programming, data manipulationData analysis, SQL, visualization tools
Work EnvironmentInternship setting, entry-level projectsFull-time, corporate or research settings
CertificationsOptional, often includes online coursesOften requires or prefers certifications in data analysis or related fields

Internship Python R roles are entry-level positions focusing on learning and assisting with data projects using Python and R. Data Analyst roles are more advanced, involving comprehensive data analysis, reporting, and decision-making. Internships serve as a stepping stone toward full Data Analyst positions, which require more experience and skills.

What are the most commonly searched types of Python R jobs in Sacramento, CA?

The most popular types of Python R jobs in Sacramento, CA are:

Internship Python R jobs near you

Biostatistics Scientist (Plant Science)

Woodland, CA • On-site

Sakata Seed America, INC.
See more jobs at Sakata Seed America, INC.external

$59K - $60K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 4 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 


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