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Research Scientist
Center for Effective Organization
USC Marshall School of Business
Since its founding in 1979, the Center for Effective Organizations (CEO), a research center in the USC Marshall School of Business, is world\-renowned for its breakthrough research and insights on organizational performance, effectiveness and talent management. Today, CEO is at the forefront, discovering and creating the latest knowledge in the design and management of organizations for companies that range from mid\-sized to the Global 500\. Our research scientists and practitioners have extensive experience bridging research and practice in organization design, change and transformation.
CEO distinguishes itself from other applied research centers and consulting organizations by partnering with organizations to rigorously assess the relevant and strategic issues facing them, applying proven interventions guided by that assessment, and evaluating the effectiveness of the engagement. For more information on the Center for Effective Organizations, visit: https://ceo.usc.edu/
Responsibilities and Duties
A Research Scientist is both a scholar and consultant. They bridge the gap between academic theory and practical business strategy. Research Scientists work closely with corporate sponsors and Fortune 500 organizations to improve organization design, talent strategy, and workforce performance.
Research Design \& Data Execution:
- Formulate research studies independently on key topics such as organization design, workforce agility, people analytics, organizational transformation, business processes and AI transformation.
- Collect complex data sets using methods like quantitative surveys, executive interviews, and direct organizational diagnostics.
- Analyze workplace patterns to identify trends in team behavior, leadership efficacy, and structural flaws in the organization design.
- Oversee compliance guidelines for human subject research and data privacy to maintain institutional integrity.
Corporate Partnership \& Executive Consulting:
- Partner with executives at large organizations to align their organizational structures with strategic goals.
- Develop actionable tools and operational frameworks that companies can use to manage internal change.
- Deliver diagnostic feedback directly to corporate sponsors and clients based on data collection from projects
- Facilitate interactive workshops and strategy sessions to help companies test new organizational theories.
- Facilitate in\-person and online custom and public workshops and labs for executive teams
Dissemination of Knowledge \& Thought Leadership:
- Publish scientific papers, and practitioner\-focused articles and editorials in top\-tier journals and business, management, and human resource outlets.
- Write industry reports or papers intended for senior executives and C\-suite readers.
- Present research findings at major academic conferences and industry executive forums.
- Host public webinars and education sessions to broadcast cutting\-edge theories to the management community.
Funding \& Program Management:
- Support business development and secure external funding through corporate sponsorships, projects, foundation grants, and research contracts.
- Manage project timelines and budgets for corporate projects and research initiatives.
- Collaborate with networks of global scholars, affiliated faculty, and industry leaders through serving on boards and attending conferences.
Preferred Qualifications:
Academic \& Educational Background
- Doctorate (Ph.D.) in Industrial\-Organizational Psychology, Organizational Behavior, Management, or highly related quantitative social science field.
- Active publishing track record with articles featured in top\-tier management and behavioral journals, and practitioner\-focused outlets.
Research \& Analytical Expertise:
- Advanced quantitative and qualitative skills
- Proficiency in use of LLM tools
- Expertise in organization design and people analytics
Corporate \& Consulting Experience:
- Executive\-level presentation skills with a proven ability to translate complex data into practical, actionable business strategies.
- Corporate background working directly with CHROs, CEOs, and senior leadership teams at Fortune 500\-level companies.
- Proven fundraising history with a track record of securing corporate sponsorships, research grants, and external funding contracts
Leadership \& Project Mastery
- Independent project management experience and supervisory experience managing staff
Anticipated Hiring Range:
The annual base salary range for this position is $117,128\.22 \- $135,000\. When extending an offer of employment, the University of Southern California considers factors such as (but not limited to) the scope and responsibilities of the position, the candidate’s work experience, education/training, key skills, internal peer equity, federal, state and local laws, contractual stipulations, grant funding, as well as external market and organizational considerations.
Required Documents and Additional Information
- Resume and cover letter required (may be uploaded as one file).
- Job openings are posted for a minimum of seven calendar days. This job may be removed from posting boards and filled any time after the minimum posting period has ended, so please apply on the same business day if interested.
- USC has excellent benefits, including health benefits for staff and their family with access to the renowned university medical network; eligibility for retirement plans with employer contributions\*; tuition benefits for staff and their family; free Professional Development classes; central Los Angeles location with easy access to commuter trains, buses and free tram pick up services; discounts to sporting and other campus events.
Why join the USC Marshall School of Business?
The USC Marshall School of Business is ideally positioned to address the challenges of a rapidly changing business environment and is ranked as one of the country’s top schools for accounting, finance, marketing, consulting, entrepreneurship and international business studies.
USC Marshall builds on the unique opportunities that stem from its Los Angeles location on the Pacific Rim, its interdisciplinary and impactful research, the momentum generated by the University of Southern California, and the unparalleled engagement of the Trojan Alumni Family. With ready access to industries defining the new business frontier, including bio\-technology, life sciences, media, entertainment, communications and healthcare, this vast network offers USC Marshall graduates exceptionally strong support for success in the global marketplace.
For more information on the USC Marshall School of Business, visit: www.marshall.usc.edu.
Minimum Education: Doctor of Philosophy (PhD) Minimum Experience: 5 years Minimum Skills: Directly related education and experience in research specialization with highly advanced knowledge of equipment, procedures, analysis methods, principles, theories and concepts. Demonstrated leadership in developing new ideas and ability to publish in appropriate academic and practitioner outlets. Demonstrated independent thinking and leadership in scholarly writing according established criteria (e.g., first\-authored publications, conceptual leadership in the development of publications generated alone or with others, writing of first drafts of publications and/or single\-authored publications).
Salary Context
This $117K-$135K range is in the lower quartile for Research Scientist roles in our dataset (median: $195K across 149 roles with salary data).
Role Details
About This Role
Research Scientists push the boundaries of what AI can do. They design experiments, develop novel architectures, publish papers, and translate research breakthroughs into production capabilities. This is where the fundamental advances happen, from attention mechanisms to diffusion models to reasoning chains.
The work is intellectually demanding and often ambiguous. You might spend months on an approach that doesn't pan out. The best research scientists combine deep mathematical intuition with engineering pragmatism. They know when to go deep on theory and when to run experiments. They read papers voraciously and can spot incremental contributions from genuine breakthroughs.
Across the 4,317 AI roles we're tracking, Research Scientist positions make up 4% of the market. At University of Southern California, this role fits into their broader AI and engineering organization.
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
What the Work Looks Like
A typical week includes: reading and discussing recent papers with your team, designing and running experiments on multi-GPU clusters, analyzing results and iterating on hypotheses, writing up findings for internal review or publication, and collaborating with engineering teams to productionize promising results. The ratio of thinking to coding is higher than in engineering roles.
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
Skills in Demand for This Role
PhD strongly preferred for most roles. Deep expertise in a specific area (NLP, computer vision, reinforcement learning, multimodal) is expected. PyTorch is the standard. Publication track record matters. Strong mathematical foundations in linear algebra, probability, optimization, and information theory are assumed.
Beyond the fundamentals, companies value experience with large-scale distributed training, novel architecture design, and the ability to bridge theory and practice. Understanding of current frontier topics (reasoning, multimodal, long-context, alignment) is essential. Code quality matters more than many researchers expect. Labs want researchers who can implement their ideas cleanly.
Strong research postings specify the research area, mention the team you'd join, and describe the problems they're working on. They often list recent publications from the team. Vague 'AI research' postings without specifics usually mean the company wants to sound impressive but doesn't have a real research agenda.
Compensation Benchmarks
Research Scientist roles pay a median of $222,200 based on 378 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($126K) sits 43% below the category median. Disclosed range: $117K to $135K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
University of Southern California AI Hiring
University of Southern California has 2 open AI roles right now. They're hiring across Research Scientist, Research Engineer. Based in Los Angeles, CA, US. Compensation range: $95K - $135K.
Location Context
AI roles in Los Angeles pay a median of $214,112 across 708 tracked positions.
Career Path
Common paths into Research Scientist roles include PhD Student, Research Engineer, Postdoc.
From here, career progression typically leads toward Research Lead, Distinguished Scientist, VP of Research.
The PhD is the entry point for most paths. Choose your advisor and research area carefully since they'll define your first industry position. Publish consistently, contribute to open-source projects in your area, and build relationships at conferences. Industry research offers better compensation and compute resources than academia, but the pressure to show product impact is real.
What to Expect in Interviews
Research interviews are multi-stage: a research talk (present your best paper), technical deep-dives on your methodology, and often a 'research proposal' exercise where you design an experiment to test a hypothesis. Coding rounds test implementation ability alongside theoretical knowledge. Be prepared to implement a paper from scratch and discuss the design choices the authors made. Strong candidates can critique papers constructively and identify gaps in experimental methodology.
When evaluating opportunities: Strong research postings specify the research area, mention the team you'd join, and describe the problems they're working on. They often list recent publications from the team. Vague 'AI research' postings without specifics usually mean the company wants to sound impressive but doesn't have a real research agenda.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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