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Weekend Data Science R Jobs in California (NOW HIRING)

Jj

Manager, Data Science - Oncology

San Diego, CA · On-site +1

Data Science Job Category: People Leader All Job Posting Locations: Cambridge, Massachusetts ... Develop Oncology R&D-specific data repositories by implementing standard enterprise-level data ...

Deloitte

Data Science Consultant

Los Angeles, CA · On-site

Proficiency in high level scripting language, such as Python or R * Understanding of the strategic application of data science methodologies in driving valuable business outcomes for large ...

1 point system

Data Science Engineer

Century City, CA · On-site

Must have excellent communication Summary The Data Scientist is a member of a highly motivated Tech ... Proficiency in using Python, R, or similar mathematical programming languages for advanced ...

Deloitte

Data Science Consultant

San Francisco, CA · On-site

Proficiency in high level scripting language, such as Python or R * Understanding of the strategic application of data science methodologies in driving valuable business outcomes for large ...

Deloitte

Data Science Consultant

Sacramento, CA · On-site

Proficiency in high level scripting language, such as Python or R * Understanding of the strategic application of data science methodologies in driving valuable business outcomes for large ...

Deloitte

Data Science Consultant

San Diego, CA · On-site

Proficiency in high level scripting language, such as Python or R * Understanding of the strategic application of data science methodologies in driving valuable business outcomes for large ...

Deloitte

Data Science Consultant

San Jose, CA · On-site

Proficiency in high level scripting language, such as Python or R * Understanding of the strategic application of data science methodologies in driving valuable business outcomes for large ...

Corvel

Data Science Manager

Irvine, CA · On-site +1

The Manager Data Science is responsible for architecting, building, and deploying production-grade machine learning models, AI solutions, and data products while leading a team of advanced data ...

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What are some common challenges faced by Weekend Data Science R professionals, and how can they effectively manage project deadlines given a limited work schedule?

Weekend Data Science R professionals often face the challenge of managing complex data analysis projects within a restricted timeframe. Balancing project deadlines with limited availability requires strong time management and clear communication with team members. It's important to set realistic goals for each work session, prioritize tasks that drive the most value, and leverage collaboration tools to stay aligned with colleagues working during weekdays. Building a habit of thorough documentation and regularly syncing with the team ensures that progress continues smoothly, even when not physically present during the standard workweek.

What is a Weekend Data Science R?

A Weekend Data Science R is typically a data scientist or analyst who specializes in using the R programming language to analyze and interpret data, and who works primarily on weekends. This role may be part-time, project-based, or designed for individuals who are balancing other commitments during the week. Weekend Data Science R professionals often handle tasks such as data cleaning, statistical analysis, and creating data visualizations using R. They are valued for their ability to deliver insights and support decision-making processes, often on a flexible schedule. This role is ideal for those who have strong analytical skills and proficiency in R, and who prefer or require weekend work hours.

What are the key skills and qualifications needed to thrive as a Weekend Data Science R, and why are they important?

To thrive as a Weekend Data Science R, you need a solid background in statistics, programming (especially in R), and data analysis, often supported by a relevant degree or coursework. Familiarity with data visualization tools, machine learning libraries, and version control systems like Git is commonly required. Excellent problem-solving, time management, and communication skills help you tackle projects independently and convey insights clearly. These skills are crucial for delivering actionable results efficiently while balancing part-time or weekend schedules.
What are the most commonly searched types of Data Science R jobs in California? The most popular types of Data Science R jobs in California are:
Weekend Data Science R jobs near you
Director, Data Science

Director, Data Science

Glyphic Biotechnologies

Berkeley, CA • On-site

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 13 days ago

Job description

About Glyphic:
At Glyphic Biotechnologies, we plan to create the protein revolution for which scientists and researchers have been waiting. We are developing a massively parallel, single-molecule proteome sequencing platform that will transform life science discovery and usher in a new era of insights into human biology and disease. To date, we have raised >$80M from venture partners and non-dilutive grant funding to achieve our vision of next generation proteome sequencing.
What we are looking for in you
We are looking for a Director-level technical leader to build and lead Glyphic's Data Science function. This is a "player-coach" role. You must be technically deep enough to guide signal-processing and ML strategy for a novel nanopore-based protein sequencing platform, while also building the team culture, processes, and infrastructure needed to later scale from a larger data organization. You will report to the VP of R&D and work closely with team leads in assay development, chemistry, and automation.
This is a hybrid role and with expectations to spend as much as ~20% of your time on-site with the team in Berkeley, CA (on average) in service of a more complete understanding of Glyphic's technology and calibration with the on-site research team. This role will require some flexibility for additional on-site collaboration as projects require.
What you'll do
Technical Leadership
  • Set the technical direction for ML model development: amino acid classification from nanopore current signals, signal segmentation, stall detection, temporal modeling, and multi-cycle analysis.
  • Drive improvements to classification accuracy through better architectures (transformers, deep learning), training strategies, and feature engineering.
  • Own the roadmap for data infrastructure: pipeline automation, data lake architecture, metadata standards, and self-serve analytics for the broader scientific team.
  • Make strategic build-vs-buy decisions for tooling, compute, and third-party platforms.

People Management
  • Provide technical and professional management to a team of data scientists and engineers to enable end-to-end analysis pipeline
  • Create an environment where high-autonomy individual contributors thrive: clear goals, minimal process overhead, rapid feedback loops.
  • Foster a culture of rigorous, reproducible analysis and clear communication of results to non-computational audiences.

Cross-Functional Partnership
  • Translate wet-lab experimental goals into computational strategies and vice versa - surface data-driven insights that reshape assay design and instrument operation.
  • Work with assay development to design experiments that generate high-quality training data and enable systematic evaluation of new chemistries (expanders, linkers, barcodes).
  • Collaborate with the Head of Automation and hardware teams on instrument data integration and real-time analysis capabilities.
  • Represent Data Science in management discussions, communicating progress, risks, and resource needs clearly.

AI Strategy
  • Champion the adoption of AI coding and analysis tools (Claude, Claude Code, etc.) across the data team and the broader organization.
  • Evaluate how generative AI and LLMs can accelerate internal workflows: automated reporting, data exploration, code generation, and literature review.

What You Need
Required:
  • MS or PhD in a quantitative field (Computer Science, Electrical Engineering, Computational Biology, Bioinformatics, Statistics, or related)
  • 10+ years of post-academic experience in the omics space (genomics, proteomics, or related fields).
  • 4+ years of experience managing technical teams (data scientists, ML engineers, or bioinformaticians), including hiring responsibility.
    • Ability and willingness to operate as a player-coach: setting strategy while remaining hands-on with data, code, and models.
    • Exceptional ability to identify, hire, and develop talent while establishing and enforcing standards of excellence in data science
    • Capacity to develop both individual contributors and future managers within the team.
  • Deep expertise in one of the following:
    • Primary sequencing data analysis
    • Machine learning applied to biological data
    • Pipeline infrastructure and bioinformatics tooling
  • Solid understanding of signal processing, classification, and machine learning techniques (transformers, CNNs, RNNs) and comfort applying them to sequencing or time-series data
  • Practical familiarity with AWS, Nextflow, and modern bioinformatics tooling.
  • Demonstrated ability to work at the bench-to-computation interface in collaborative research environments
  • Ability to present complex technical results to non-technical stakeholders and to translate biological questions into computational approaches.

Nice to have:
  • Direct experience with sequencing data, basecalling, read-level QC or nanopore signal-level analysis (strongly preferred).
  • Experience building data infrastructure and analytics platforms in early-stage biotech.

We're looking for a teammate that:
  • Navigates complex team dynamics, partnerships, and challenges with creativity and logic.
  • Operates with adaptability, urgency, and flexibility in evolving environments, thriving in ambiguity.
  • Drives work forward without needing to be asked, taking responsibility for outcomes rather than tasks.
  • Treats obstacles as problems to be creatively solved, not reasons something can't be done.
  • Applies sound judgment to the best available information, testing, learning, and iterating.
  • Shares early and directly when assumptions change, results are unclear, or timelines are at risk.

What you can expect from this role
Work environment:
  • Collaborative culture where your ideas and expertise are valued
  • Direct impact on product development and company direction

Professional growth:
  • Build Glyphic's first dedicated data science management function, defining team structure, standards, and culture.
  • Help define the technical standards and best practices for omics data analysis while mentoring the next generation of data scientists who will adopt and advance these approaches.
  • Work with proprietary, information-rich data at scale that few organizations possess-the opportunity to develop novel approaches and methodologies that set benchmarks for the field.

Compensation
Estimated Base Salary: $215,000 - $257,000/year
This is the pay range for this position that we reasonably expect to pay. Individual compensation is based on various factors including, experience, education, skillset, and geographic location. This range is for the SF Bay Area, California location and may be adjusted to the labor market in other geographic areas. We are open to considering compensation above this range for candidates whose background and expertise exceed our expectations for the role.
Benefits and Perks:
  • Employee Stock Option Plan
  • 100% Health Plan Coverage for Employees & Dependents (Medical, Dental, & Vision)
  • Employer Retirement Contributions to 401(k)
  • Generous Paid Time Off
  • Paid Maternity and Paternity Leave
  • Health & Wellbeing Program
  • Office Snacks and Beverages
  • Regular Team Bonding Activities

We are an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. Individuals seeking employment at Glyphic Biotechnologies are considered without regard to race, color, religion, national origin, age, sex, marital status, ancestry, physical or mental disability, veteran status, gender identity, or sexual orientation.

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