data scientist, product analytics

clinical lab scientist generalist Jobs in San Francisco

clinical lab scientist generalist

clinical laboratory scientist generalist Jobs in San Francisco

clinical laboratory scientist generalist

clinical research coordinator Jobs in San Francisco

clinical research coordinator

clinical research coordinator - emergency medicine Jobs in San Francisco

research scientist

OpenAI

About the Team
<br><br>
The Future of Computing Research team is an applied research team within the Consumer Devices group focused on developing new methods, models, and evaluation frameworks that support our vision for the future of computing. We work at the frontier of multimodal AI, helping turn emerging model capabilities into product experiences that are useful, delightful, and worthy of long-term trust.
<br><br>
Our work explores a new class of AI systems that can learn over time, adapt to individuals, and support people in the flow of daily life. This includes long-term memory, user modeling, and personalization systems that are aligned not just with immediate satisfaction, but with a person’s broader goals, values, and well-being.
<br><br>
We work closely across research, engineering, design, product, and safety to define what it means to build AI systems that know you over time, act at the right moment, and help in ways that are context-aware, respectful, and demonstrably beneficial.
<br><br>
About the Role
<br><br>
We are looking for a Research Engineer / Scientist to join the Future of Computing Research team to work on RLHF and post-training for personalized, multimodal AI systems.
<br><br>
This role will focus on building the learning and evaluation foundations that help models become more context-aware, adaptive, and useful over time. You will work on problems such as reward modeling, preference learning, long-horizon evaluation, and policy improvement for systems that must make high-quality behavioral decisions in realistic user settings. The work is deeply product-grounded: success is not just higher benchmark performance, but better model behavior in real-world use.
<br><br>
The ideal candidate is excited about pushing beyond one-turn assistant behavior toward systems that improve through feedback, learn from richer signals, and are trained against meaningful notions of user value. Internally, that maps closely to the need for careful reward design, feedback loops, and evaluation frameworks that test whether interventions are actually beneficial over longer horizons.
<br><br>
This role is based in San Francisco, CA. We use a hybrid work model of four days in the office per week and offer relocation assistance to new employees.
<br><br>
<b>In this role, you will:</b><br>
Develop RLHF and post-training methods for multimodal models.
<br><br>
Build reward models and preference-learning pipelines for adaptive, personalized model behavior.
<br><br>
Design datasets, rubrics, and evaluation frameworks that capture user preferences, contextual appropriateness, and long-term value in realistic tasks.
<br><br>
Run experiments on policy improvement using explicit feedback, implicit signals, and model-based grading.
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Work on long-horizon evaluation problems, where model quality depends not just on a single response but on whether behavior improves outcomes over time.
<br><br>
Collaborate closely with safety researchers to ensure that adaptation and personalization remain aligned, interpretable, and bounded by clear constraints.
<br><br>
Prototype and iterate quickly on training recipes, reward formulations, data pipelines, and evaluation suites for product-relevant behaviors.
<br><br>
Help define how OpenAI measures success for personalized AI systems including trust, appropriateness, and long-term user benefit.
<br><br>
<b>You might thrive in this role if you:</b><br>
Have a strong background in machine learning research, with experience in RLHF, reward modeling, preference optimization, or post-training for large models.
<br><br>
<b>Have worked on one or more of:</b> reinforcement learning, ranking, recommender systems, personalization, memory, or human-in-the-loop evaluation.
<br><br>
Care about rigorous empirical work and know how to design clean experiments, reliable evals, and decision-useful metrics.
<br><br>
Are excited by the challenge of training models against nuanced behavioral objectives.
<br><br>
Have experience building datasets or eval pipelines grounded in human preferences, rubrics, or real-world product behavior.
<br><br>
Are comfortable working across the stack, from data generation and labeling strategy to training runs, reward functions, and analysis.
<br><br>
Are interested in multimodal AI and in how models can learn from richer interaction signals over time.
<br><br>
Want to work on product-shaping research with unusually high stakes for trust, alignment, and long-term user value.
<br><br>
Enjoy close collaboration with engineers, designers, and safety researchers to turn frontier research into real systems.
<br><br>
About OpenAI
<br><br>
OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.
<br><br>
We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic.
<br><br>
For additional information, please see
<br><br>
OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement
<br><br>
.
<br><br>
Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations.
<br><br>
To notify OpenAI that you believe this job posting is non-compliant, please submit a report through
<br><br>
this form
<br><br>
. No response will be provided to inquiries unrelated to job posting compliance.
<br><br>
We are committed to providing reasonable accommodations to applicants with disabilities.
<br><br>
OpenAI Global Applicant Privacy Policy
<br><br>
At OpenAI, we believe artificial intelligence has the potential to help people solve immense global challenges, and we want the upside of AI to be widely shared. Join us in shaping the future of technology.
<br><br>
Compensation
<br><br>
$380K – $445K + Offers Equity

Research Engineer/Scientist - Human Alignment, Consumer Devices

Uber

<div><b>About the Role</b>
<br>
<br>
The fares platform team powers the fares for all of Uber - that's all riders, earners and eaters across the Uber and Uber Eats app. With $190B+ in gross bookings processed by the fares platform annually, we solve high impact problems that are critical to Uber's success.
<br>
<br>
As a Scientist on this team, you will leverage experiment design, exploratory data analysis, causal inference and model development to solve a wide variety of problems in domains like pricing, policy design, optimization and defect reduction.
<br>
<br>
<b>What the Candidate Will Need / Bonus Points</b>
<br>
<br>
<b>What the Candidate Will Do:<br>
</b><br>
<br>
<ul><li>Refine ambiguous questions and generate data backed hypotheses through a deep understanding of the data, systems, customers and business.
</li><li>Use experiments and causal inference methods to validate the hypothesis and drive business goals critical to Uber's success.
</li><li>Act as a thought partner with cross-functional teams across various disciplines, including product, engineering, and operations, to drive product strategy. Influence the product roadmap for fares and several cross-organizational teams.
</li><li>Build ML models to solve complex problems and create AI agents to automate data science workflows.
</li>Communicate findings clearly to technical and non-technical leadership to influence decisions and product direction.<br>
<li>
</li></ul><br>
<br>
<b>Basic Qualifications:</b><br>
<br>
<br>
<ul><li>Ph.D., M.S., or Bachelors degree in Statistics, Economics, Machine Learning, Computer Science, or other quantitative fields.
</li><li>Minimum 5 years of industry or academic experience as an Applied or Data Scientist or equivalent (with at least two of those years in industry).
</li><li>Experience with experiment design, exploratory data analysis, causal inference and model development.
</li>Proficient in both a data ETL language (e.g. SQL) and a scripting language (e.g. Python, R).<br>
<li>
</li></ul><br>
<br>
<b>Preferred Qualifications:
</b><br>
<ul><li>Minimum 7 years of industry experience as an Applied or Data Scientist or equivalent.
</li><li>Experience using statistical methodologies in platform, marketplace or consumer domains.
</li><li>Demonstrated ability to leverage data and systems thinking for storytelling and root cause analysis.
</li>Excellent communication skills: able to lead initiatives across multiple product areas and communicate findings with leadership.<br>
<li>
</li></ul><br>
<br>
For New York, NY-based roles: The base salary range for this role is USD$190,000 per year - USD$211,000 per year. For San Francisco, CA-based roles: The base salary range for this role is USD$190,000 per year - USD$211,000 per year. For Seattle, WA-based roles: The base salary range for this role is USD$190,000 per year - USD$211,000 per year. For Sunnyvale, CA-based roles: The base salary range for this role is USD$190,000 per year - USD$211,000 per year. For all US locations, you will be eligible to participate in Uber's bonus program, and may be offered an equity award &amp; other types of comp. All full-time employees are eligible to participate in a 401(k) plan. You will also be eligible for various benefits. More details can be found at the following link https://jobs.uber.com/en/benefits.</div>

Senior Scientist, Fares

General Medicine

<div><h1 class="jobSectionHeader"><b>Marketplace Scientist
</b></h1><p></p><h1 class="jobSectionHeader"><b>About General Medicine</b></h1><p>
We&rsquo;re building a platform that makes it delightfully simple for people to find, book, and afford care across virtual and in-person visits, prescriptions, labs, imaging, and more.
</p><p>As a Marketplace Scientist at General Medicine, you&rsquo;ll work directly with our Chief Economist to design the algorithms, mechanisms, and models that underpin the matching, pricing, and insurance estimation systems at the heart of the business. From understanding patient preferences to forecasting prices across complex insurance networks, your work will determine how the marketplace functions and scales.</p><p></p><h2 class="jobSectionHeader"><b>
What we&rsquo;re looking for</b></h2><p>
As a Marketplace Scientist at General Medicine, you&rsquo;ll work directly with our Chief Economist to design the algorithms, mechanisms, and models that underpin the matching, pricing, and insurance estimation systems at the heart of the business. From understanding patient preferences to forecasting prices across complex insurance networks, your work will determine how the marketplace functions and scales.</p><p></p><h2 class="jobSectionHeader"><b>
You should be excited to:</b></h2><ul><li><p>
Own our insurance price estimation system end to end &ndash; defining the roadmap, identifying and acquiring new data sources, and moving us from a rules-based engineering solution to a Bayesian statistical model that returns calibrated estimates with confidence intervals even where data is incomplete.</p></li><li><p>
Translate statistical models into concrete product milestones, sequencing the work across incremental launches and collaborating with engineering to get things into production.</p></li><li><p>
Work with rich but messy operational datasets &ndash; cleaning, joining, interpreting, and stress-testing them &ndash; and make the case for investing in new external sources where the evidence supports it.</p></li><li><p>
Rebuild forecasting infrastructure that is currently limited, supporting capacity planning, patient routing, and network design.</p></li><li><p>
Run counterfactual analyses and empirical studies to validate model designs and inform decisions on ad spend, utilisation, and patient direction.</p></li></ul><p></p><h2 class="jobSectionHeader"><b><br>
Ideal Qualifications</b></h2><ul><li><p>
4&ndash;7 years of experience in a quantitative field (Statistics, Economics, Math, Computer Science, Physics, or similar)</p></li><li><p>
Undergraduate degree with strong quantitative rigor (economics, applied math, engineering, computer science, physics, or similar); Master&rsquo;s or PhD preferred</p></li><li><p>
Advanced fluency in Python or R, including experience working with large, messy datasets and building production-quality analyses</p></li><li><p>
Comfort with at least one programming or analytical tool beyond SQL/Excel (e.g., Python, R, dbt, BI tooling)</p></li><li><p>
Strong statistical intuition and ability to reason from first principles</p></li><li><p>
Experience building production-facing models or decision systems, not just exploratory analyses</p></li><li><p>
Comfort working with large, imperfect real-world datasets such as insurance claims or reimbursement files</p></li><li><p>
A clear, demonstrated history of academic and professional excellence</p></li><li><p>
High ownership and ability to operate in ambiguous, fast-moving environments</p></li><li><p>
Startup-ready mindset: resourceful, hands-on, and energized by ambiguity.</p></li></ul><p><b>
This is a hybrid role in either SF or Boston (Cambridge).</b></p></div><p></p>

Marketplace Scientist

Omnifold

<div><p><b>Member of Technical Staff, ML Research</b></p><p>
Omnifold trains custom AI models for each customer's supply chain - purpose-built systems that forecast demand, optimize decisions, and adapt continuously to a changing world. The research team is responsible for the core intelligence that makes this possible: developing new model architectures, curating proprietary data assets, and pushing the boundaries of what ML can do.</p><p><b>
What makes this job interesting:</b></p><ul><li><p>
You will work on problems that frontier models can't solve. Supply chain dynamics require modeling physical systems and processes.</p></li><li><p>
You will own the full research cycle, from hypothesis to production model, with direct visibility into real-world impact.</p></li><li><p>
You will work at the intersection of statistical models, LLM reasoning capabilities, and proprietary data - a combination few research teams are building</p></li></ul><p><b>
What you'll own:</b></p><ul><li><p>
Designing and training models for forecasting and optimization across complex, multi-variable supply chain environments</p></li><li><p>
Building and curating proprietary data assets that carry signal about real-world physical and commercial systems</p></li><li><p>
Integrating LLM knowledge and reasoning capabilities into purpose-built models to maximize accuracy and adaptability</p></li><li><p>
Continuously improving model performance as market conditions shift (consumer sentiment, product launches, geopolitical changes, competitive dynamics)</p></li></ul><p><b>
What we're looking for:</b></p><ul><li><p>
Strong foundations in machine learning &mdash; experience developing and evaluating forecasting models, and LLM pipelines</p></li><li><p>
Deep understanding of time-series forecasting, optimization, or related domains</p></li><li><p>
Experience working with messy, heterogeneous real-world data</p></li><li><p>
PhD or equivalent research experience preferred, but exceptional engineers with relevant industry experience will be considered</p></li><li><p>
Comfort operating in a fast-moving, early-stage environment where research directly feeds production systems</p></li></ul><p><b>
Location:</b> San Francisco (in-person, 5 days per week)</p><p><b>
Omnifold&rsquo;s Mission</b></p><p>
Every bad forecast has a physical consequence. Unnecessary goods are manufactured, shipped, and stored. Emergency air freight is needed for misallocated products. Poor production planning means workers show up with nothing to do, or work frantic overtime. Inefficiency is everywhere.</p><p>
Our mission is to eliminate waste and accelerate growth for every company with physical products.</p></div>

Research Engineer

<div><b>About the Role</b>
<br>
<br>
Have you ever ordered a car service on Uber, and when the ride arrives, wondered how it got to you so fast? Ever ordered food on UberEats and wondered where the driver was before receiving your order and how long it took to get to the restaurant or if your order was ready when the courier arrived? Ever wondered why your grocery delivery from Uber always has the best apple picked?
<br>
<br>
If so, Uber is for you. In our ML and Science division, we strive to make magic within Uber's marketplace. This requires judgment to make difficult trade-offs, blending algorithms with human resourcefulness, and the ability to build simplicity from complexity. When we get the balance right for everyone, Uber magic happens. We build systems to peer into the future to craft the most cost-efficient marketplace for matching supply and demand. We are passionate about using innovative economics, machine learning, and scalable distributed software that automates and optimizes every aspect of this intricate dance between participants of the marketplace.
<br>
<br>
We are involved in every stage of the product development cycle and use data to inform product decisions, build models to power our solutions, and also develop platform tools that are used across teams with a primary focus on Mobility and Delivery. We work with millions of earners across the globe to make this magic happen and want you to join us!
<br>
<br>
<b>About the Team</b>
<br>
<br>
Earners (drivers and couriers) are an integral part of Uber's multi-sided marketplace. They provide the time and the means to move people and things. Importantly, they enable the connection between the physical and digital world to make the movement happen at the push of a button for everyone, everywhere.
<br>
<br>
Within Uber, Earner plays a critical role in earners' journey as the team is responsible for earner onboarding, activation, early life cycle, and resurrection. This presents the teams with the opportunity to shape and tailor the product experience during earners' many firsts (i.e., first time interacting on the Uber platform, choosing the earning opportunity, going online, receiving incentive offers, completing a trip, or reading the earnings summary). These firsts can be daunting.
<br>
<br>
Therefore, making sure that the earner journey is great at every touch point is important to build trust with Earners, communicate Uber's value proposition, and ensure each firsts to be a great experience.
<br>
<br>
The team employs a variety of ML/AI techniques, spanning from causal ML meta learners, supervised ML, RL multi-armed bandits, genAI LLMs, transformer modeling on sequential data to deep learning embeddings to build impactful data products.
<br>
<br>
<b>What the Candidate Will Need / Bonus Points</b>
- What the Candidate Will Do -<br>
<br>
<br>
<ul><li>Build statistical, optimization, and machine learning models
</li><li>Develop innovative new earner incentives that earners for choosing our network and optimizing Uber's new earner incentives spend
</li><li>Optimize Uber's background check spend and onboarding funnel
</li><li>Design recommendation engines to recommend the most relevant earning opportunities and early lifecycle content
</li><li>Develop matching algorithms for driver to driver mentorship program
</li><li>Model and predict earner behaviors to improve earner experience throughout the onboarding funnel
</li><li>The team employs a variety of ML/AI techniques, spanning from causal ML meta learners, supervised ML, RL multi-armed bandits, genAI LLMs, transformer modeling on sequential data to deep learning embeddings to build impactful data products.
</li><li>Work closely with multi-functional leads to develop technical vision, new methodological approaches, and drive team direction.
</li><li>Collaborate with cross-functional teams such as product, engineering, operations, and marketing to drive ML system development end-to-end from conceptualization to final product.
</li></ul>- Basic Qualifications -<br>
<br>
<br>
<ul><li>PhD, Master or equivalent experience in Computer Science, Machine Learning, Operations Research, Statistics, or other related quantitative fields or related field
</li><li>2 years minimum of industry experience as a Machine Learning Engineer/Research Scientist with a strong focus on deep learning and probabilistic modeling.
</li><li>Proficiency in multiple object-oriented programming languages (e.g. Python, Go, Java, C++).
</li><li>Experience with any of the following: Spark, Hive, Kafka, Cassandra.
</li><li>Experience building and productionizing innovative end-to-end Machine Learning systems.
</li><li>Experience in exploratory data analysis, statistical modeling, hypothesis testing, and experimental design.
</li><li>Experience working with cross-functional teams(product, science, product ops etc).
</li></ul>- Preferred Qualifications -<br>
<br>
<br>
<ul><li>3+ years of industry experience in machine learning, including building and deploying ML models.
</li><li>Publications at industry recognized ML conferences.
</li><li>Experience in modern deep learning architectures and probabilistic modeling.
</li><li>Experience with optimization techniques, including reinforcement learning (RL), Bayesian methods, causal ML meta learners, genAI LLM.
</li>Expertise in the design and architecture of ML systems and workflows.<br>
<li>
</li></ul><br>
<br>
<br>
<br>
For New York, NY-based roles: The base salary range for this role is USD$171,000 per year - USD$190,000 per year. For San Francisco, CA-based roles: The base salary range for this role is USD$171,000 per year - USD$190,000 per year. For Seattle, WA-based roles: The base salary range for this role is USD$171,000 per year - USD$190,000 per year. For Sunnyvale, CA-based roles: The base salary range for this role is USD$171,000 per year - USD$190,000 per year. For all US locations, you will be eligible to participate in Uber's bonus program, and may be offered an equity award &amp; other types of comp. All full-time employees are eligible to participate in a 401(k) plan. You will also be eligible for various benefits. More details can be found at the following link https://jobs.uber.com/en/benefits.</div>

Machine Learning Engineer II

University of California - San Francisco

<p></p><div><div><div><div>Senior Clinical Research Coordinators independently coordinate and are accountable for the overall administration of one or more clinical studies, typically requiring advanced-level knowledge and skills. They may provide leadership to lower-level clinical research coordinators and/or other support personnel.<br>
<br>
The position is responsible for, and critically important to the overall operational management of clinical research activities. It has direct responsibility for implementing a diverse portfolio of research activities for one or more studies which may include multicenter clinical trials (both NIH and industry-sponsored), local investigator-initiated clinical trials, and/or programmatic clinical research activities, such as a clinical database and biorepository. Positions at this level may perform expert and efficient integration of multiple complex programmatic activities, applying a sophisticated medical knowledge base, substantial leadership skills, effectively working with a variety of people and organizations at UCSF (clinicians, investigators, clinical staff, administration, Committee on Human Research, human resources, contracts and grants) and elsewhere (industry sponsors, coordinating centers, FDA, NIH), exhibiting an in-depth knowledge of specific programs.<br>
<br>
This person may have central responsibility for ensuring IRB approval and compliance, contract generation, negotiation and approval, subject recruitment, appropriate study testing, and adequate enrollment of multicenter clinical trials, longitudinal cohort studies, local investigator-initiated studies, and biorepository efforts; exercise strong administration and management leadership of clinical research, strong research coordination, and may lead experienced personnel to ensure optimal systems for efficiency, compliance, safety, financial oversight, and contractual expertise in negotiations and execution, as well as create and implement quality improvement processes. This is a position of leadership whose main purpose is to ensure that clinical research commitments are met and that the programs maintain a reputation for excellence in all activities.<p><br>
The final salary and offer components are subject to additional approvals based on UC policy.</p><p>
Your placement within the salary range is dependent on a number of factors including your work experience and internal equity within this position classification at UCSF. For positions that are represented by a labor union, placement within the salary range will be guided by the rules in the collective bargaining agreement.</p><p>
The salary range for this position is $41.72 - $67.10 (Hourly Rate).</p><p>
To learn more about the benefits of working at UCSF, including total compensation, please visit: https://ucnet.universityofcalifornia.edu/compensation-and-benefits/index.html</p></div>
</div><div><p><b>Department Description</b></p>
<div>Throughout the Department of Neurology at UCSF, our mission is to deliver superb patient care, to apply state-of-the-art translational research methods to discover the causes of, and treatments for human nervous system disorders, and to educate each generation of medical students, neurology residents and postdoctoral fellows. By fostering cross-disciplinary interactions among scientists around the world, we accelerate the pace of discovery and champion the University's global health initiatives.</div>
</div><div><p><b>Required Qualifications</b></p>
<div><ul><li>Bachelor&rsquo;s degree in related area and/or equivalent experience/training.</li><li>
Demonstrated ability to develop recruitment strategies in complex environments and differing patient pools, independently identifying recruitment leads, implementing new strategies, and continually adjusting strategies to meet current recruitment goals.</li><li>
In-depth knowledge of clinical research contracts and grants, clinical trials, protocols, FDA regulations, and complex visit structures.</li><li>
Project management skills to oversee the administrative operations of the clinical trials program; knowledge of strategies for recruiting human subjects.</li><li>
Exceptional analytical skills to evaluate the importance and urgency of problems; excellent demonstrated interpersonal, verbal and written communication skills.</li><li>
Ability to analyze complex and non-routine issues requiring innovative solutions.</li><li>
Ability to operate effectively in a changing organizational and technological environment.</li><li>
Ability to assess and respond to audit risk; ability to develop project plans/schedules and motivate team to meet deadlines.</li><li>
Ability to interpret and apply policies and regulations.</li><li>
Financial analysis skills; demonstrated skills in preparing and presenting financial and quantitative analyses, including financial projections and budgets.</li><li>
Significant project and clinical trial management experience or progressively responsible experience in a related position including regulatory and compliance responsibilities.</li><li>
Experience using database programs for clinical studies, such as REDCap</li></ul></div>
</div><div><p><b>Preferred Qualifications</b></p>
<div><ul><li>Current certification by the Society of Clinical Research Associates or the Association of Clinical Research Professionals.</li><li>
Good Clinical Practices (GCP) training</li><li>
Current Basic Life Support Training (CPR) certificate</li><li>
Prior experience in investigational drug clinical research (i.e. FDA clinical trials)</li><li>
Prior experience with patient recruitment and study conduct in the neonatal intensive care unit</li><li>
Fluency in spreadsheet software; ability to develop complex spreadsheets independently; demonstrated skills in audit preparation, execution, and follow-up.</li><li>
Experience in risk management regarding study subjects, federal and non-federal sponsors and regulating bodies.</li><li>
Experience with grant application procedures, personnel documentation, contract generation and subcontracting, preferably at UCSF.</li><li>
2 years of experience as a clinical research coordinator at UCSF or similar clinical researcher role</li></ul></div>
</div><div><p><b>About UCSF</b></p>
<div>The University of California, San Francisco (UCSF) is a leading university dedicated to promoting health worldwide through advanced biomedical research, graduate-level education in the life sciences and health professions, and excellence in patient care. It is the only campus in the 10-campus UC system dedicated exclusively to the health sciences. We bring together the world&rsquo;s leading experts in nearly every area of health. We are home to five Nobel laureates who have advanced the understanding of cancer, neurodegenerative diseases, aging and stem cells.</div>
</div><div><p><b>Pride Values</b></p>
<div>UCSF is a diverse community made of people with many skills and talents. We seek candidates whose work experience or community service has prepared them to contribute to our commitment to professionalism, respect, integrity, diversity and excellence &ndash; also known as our PRIDE values.
<br>
<br>
In addition to our PRIDE values, UCSF is committed to equity &ndash; both in how we deliver care as well as our workforce. We are committed to building a broadly diverse community, nurturing a culture that is welcoming and supportive, and engaging diverse ideas for the provision of culturally competent education, discovery, and patient care. Additional information about UCSF is available at diversity.ucsf.edu
<br>
<br>
Join us to find a rewarding career contributing to improving healthcare worldwide.</div>
</div><div><p><b>Equal Employment Opportunity</b></p>
<div>The University of California is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, protected veteran status, or other protected status under state or federal law.</div>
</div><div><p><b>Organization</b></p>
<div>Campus</div>
</div><div><p><b>Job Code and Payroll Title</b></p>
<div>007889 CLIN RSCH CRD SR NEX
</div></div><div><p><b>Job Category</b></p>
<div>Research and Scientific, Supervisory / Management</div>
</div><div><p><b>Bargaining Unit</b></p>
<div>University Professional Technical Employees &ndash; Research Support Professionals Unit (UPTE-RX)
</div></div><div><p><b>Employee Class</b></p>
<div>Career</div>
</div><div><p><b>Percentage</b></p>
<div>100%</div>
</div><div><p><b>Location</b></p>
<div>San Francisco, CA
</div></div><div><p><b>Campus</b></p>
<div>Mission Bay (SF)
</div></div><div><p><b>Work Style</b></p>
<div>Fully On-Site</div>
</div><div><p><b>Shift</b></p>
<div>Days</div>
</div><div><p><b>Shift Length</b></p>
<div>8 Hours
</div></div><div><p><b>Additional Shift Details</b></p>
<div>Monday &ndash; Friday, 8am &ndash; 5pm</div></div></div></div><div></div>

Senior Clinical Research Coordinator

Data Scientist, Product Analytics

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