Business Intelligence Associate Internships
Business intelligence associate internships give university students, recent graduates, and early-career switchers hands-on project experience working alongside working data analysts and BI developers, and, at many employers, a path toward a full-time offer. Openings are concentrated across Banking & Financial Services and Consulting & Professional Services, with Navy Federal Credit Union, Sandia National Laboratories, and Inter Miami CF among the employers posting roles now.
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Showing 5 of 55+ Business Intelligence Associate Internships
[2027 Summer] AI Research and Engineering Intern for Business Intelligence (8023)
Overview of Role
Frontier models can now reason, write code, and use tools. The next step is getting agents to work reliably over long tasks with real data, and serving them efficiently in production. Enterprise business intelligence is one of the hardest places to test this. Agents must work through messy schemas and ambiguous questions. They have to keep business logic consistent across many steps, and they have to give answers someone can check. At a company operating at TSMC's scale, wrong numbers have real costs.
AI4BI Innovation center is looking for exceptional PhD and Master's students to work on this problem with us. You will own a research and engineering question end-to-end. That means designing the method, building the agent harness and environment, training or adapting models, deploying them, and measuring progress with evaluations that hold up. The goal is a first-author paper at a top venue (e.g., NeurIPS, ICLR, ICML, ACL, EMNLP) or a prototype that ships into real decision-making workflows for business stakeholders. Many projects do both.
This is not a typical software engineering internship. You will have dedicated mentorship from AI Scientists and Data engineers, and access to realistic enterprise-scale data and business problems that are hard to get in academia.
What You'll Work On
Your project will fall within one or more of these directions. Each is grounded in real business problems.
- Agent harnesses and long-horizon agents. Design the harness around the model: the agent loop, tool and sandbox interfaces, context management and compaction, memory, sub-agent delegation, permissions, and error recovery. Build agents that can investigate a business question across hours of work. They explore data, form hypotheses, run analyses, and produce a defensible report.
- Tool use and multi-agent systems. Design agents that operate over warehouses, semantic layers, dashboards, and internal APIs through standard tool protocols such as the Model Context Protocol (MCP). Study when multi-agent orchestration helps a single strong agent, and when it gets in the way.
- Pretrained time-series foundation models. Evaluate, adapt, and fine-tune pretrained time-series foundation models (e.g., TimesFM, Chronos, Moirai) for forecasting demand, capacity, and supply-chain signals. Topics include zero-shot versus fine-tuned performance, covariates and multivariate forecasting, probabilistic forecasts and calibration, and domain-adaptive pretraining on enterprise time series. Study how LLM agents can call, combine, and explain these forecasting models, alongside optimization and network science methods.
- AI infrastructure and deployment. Build the systems that take models and agents from prototype to production. Topics include efficient LLM serving and inference optimization (batching, KV-cache management, quantization, speculative decoding), model routing and cost/latency trade-offs, on-premise and secure deployment, and observability and tracing for agent runs (including guardrails and monitoring).
- Agent environments and evaluation science. Build realistic, reproducible environments and benchmarks for data agents. Develop reliable LLM-as-judge and rubric-based evaluators. Measure what matters: correctness, consistency, calibration, latency, cost, and trustworthiness.
- Synthetic data and post-training. Generate verifiable training and evaluation tasks at scale. Adapt models to the enterprise domain through fine-tuning, distillation into small, cost-efficient models that can be deployed, and, where useful, reinforcement learning from verifiable signals such as SQL results and metric consistency checks.
Responsibilities
- Lead a research and engineering project. Frame an open problem, form hypotheses, and run careful experiments, from small-scale ablations up to training and serving on GPUs.
- Develop models and agents. Build and manage advanced machine learning, deep learning, LLM, foundation model, and time-series methods. Use them to develop data-driven solutions to complex business problems, and implement agent harnesses, tool interfaces, and environments grounded in realistic BI workloads.
- Deploy and operate. Package, serve, and monitor models and agents in production-like environments. Optimize them for reliability, latency, and cost.
- Turn data into decisions. Analyze and interpret complex data sets. Translate the results into actionable insights that improve decision-making for business stakeholders.
- Evaluate rigorously. Design benchmarks and evaluation protocols that measure reliability, not just demo quality. Report negative results honestly.
- Collaborate across teams and borders. Work with cross-functional and international teams in the US and Taiwan to find and prioritize opportunities to use data. Partner with research scientists and data engineers to develop and deploy predictive models and agentic systems. Help build and maintain efficient processes for collecting, storing, and analyzing data. Take part in weekly research syncs, paper reading groups, and brainstorming sessions.
- Communicate your work. Present insights and recommendations to both technical and non-technical stakeholders through internal reports and presentations. Write up findings for publication and/or internal deployment.
Minimum Qualifications
- Education: Currently pursuing a Ph.D. or M.S. in Computer Science, Electrical Engineering, Information Systems, Information Science, Statistics, or a related AI/ML field.
- AI-enabled analytics knowledge: Depth in one or more of machine learning, deep learning, large language models, AI agents, time-series modeling and forecasting, optimization, network science, or ML systems.
- Engineering: Strong Python skills and hands-on experience building and shipping ML or LLM systems.
- Research ability: Able to read recent papers critically, then implement and extend them quickly.
Preferred Qualifications
- Publications in leading AI conferences (e.g., NeurIPS, ICLR, ICML, ACL, KDD, MLSys) or strong pre-prints in LLMs, agents, time series, ML systems, or data management / ML for databases.
- Experience designing agent harnesses or building agents with modern agent SDKs and tool protocols (e.g., MCP), including context management, tool sandboxing, and agent tracing/observability.
- Experience with pretrained time-series foundation models (e.g., TimesFM, Chronos, Moirai) or deep forecasting methods, including fine-tuning and probabilistic evaluation.
- Experience with LLM serving and inference engines (e.g., vLLM, SGLang, TensorRT-LLM), model quantization, or inference optimization.
- Experience with MLOps and deployment infrastructure (e.g., Docker/Kubernetes, Ray, CI/CD for models, monitoring), including on-premise or GPU cluster environments.
- Hands-on LLM experience beyond prompting, such as fine-tuning, preference optimization, distillation, or RL post-training.
- Experience deploying models into user-facing tools, such as dashboarding platforms (e.g., Power BI), Python prototyping frameworks (e.g., Streamlit, Dash), and/or JavaScript frameworks (e.g., React).
- Experience with text-to-SQL, semantic parsing, tabular reasoning, forecasting, or analytics.
- Contributions to open-source AI/ML projects or public benchmarks.
Company Description
Join TSMC Technology, Inc. (TTI), an innovation hub of Taiwan Semiconductor Manufacturing Company Limited (TSMC). TSMC has been a pioneer in the semiconductor industry since 1987. With teams located in San Jose, CA, and Austin, TX, TTI is dedicated to advancing semiconductor technology through expertise in physical design, memory design, analog mixed signal, power management, and groundbreaking computational AI machine learning research. Our mission is to drive the future of semiconductor innovation through unparalleled R&D and customer-focused IP. Be part of a team that shapes the next generation of technological advancements.
As a trusted technology and capacity provider, TSMC is driven by the desire to be:
- The world’s leading dedicated semiconductor foundry
- The technology leader with a strong reputation for manufacturing excellence
- Advancing semiconductor manufacturing innovations to enable the future of technology
TSMC pioneered the pure-play foundry business model when it was founded in 1987 and has been the world’s leading dedicated semiconductor foundry ever since. The Company supports a thriving ecosystem of global customers and partners with the industry’s leading process technologies and a portfolio of design enablement solutions to unleash innovation for the global semiconductor industry. With global operations spanning Asia, Europe, and North America, TSMC serves as a committed corporate citizen around the world.
In North America, TSMC has a strong sales and service organization that works with customers by helping them achieve silicon success with cutting-edge technologies and manufacturing excellence. The Company has continued to accelerate its R&D investment and staffing in recent years and is expanding its manufacturing footprint to support customer innovation with 3D IC technologies and optimal manufacturing capacity.
For positions requiring access to technical data subject to export control regulations, including Export Administration Regulations, TSMC Technology Inc. may have to obtain export licensing approval from the U.S. Government for certain individuals. All employment is contingent upon TSMC Technology Inc. obtaining any export license or other approval that may be required by the U.S. Government.
TSMC Core Values
At TSMC, we invite talented individuals who share our vision and values to contribute to our continued success. Our core values are the foundation of our culture, guiding every decision and action. We seek applicants who deeply resonate with these principles and embody them in their work:
- Integrity: Tell the truth. We do not brag. We do not make commitments lightly. Once we make a commitment, we devote ourselves completely to meeting that commitment.
- Commitment: Employees are dedicated to the company, view the company’s success as their own and work diligently to make their best contributions. As commitment is mutual, the Company strives to serve the best interests of its employees.
- Innovation: Innovation is the wellspring of the Company's growth. It means more than new ideas; it means putting ideas into practice.
- Customer Trust: We strive to build deep and enduring relationships with our customers, who trust and rely on us to be part of their success over the long term.
(For more details on TSMC’s Core Values, please visit: https://www.tsmc.com/english/aboutTSMC/values)
Diversity statement
TSMC Technology, Inc. is committed to employing a diverse workforce and provides Equal Employment Opportunity for all individuals regardless of race, color, religion, gender, age, national origin, marital status, sexual orientation, gender identity, status as a protected veteran, genetic information, or any other characteristic protected by applicable law.
TSMC is an equal opportunity employer prizing diversity and inclusion. We are committed to treating all employees and applicants for employment with respect and dignity. If you require reasonable accommodation due to a disability during the application or the recruiting process, please feel free to notify us at g_accommodations@tsmc.com. TSMC confirms to all applicants its commitment to meet TSMC’s obligations under applicable employment law. Reasonable accommodations will be determined on a case-by-case basis.
Pay Transparency Statement
At the time of this posting, this role typically pays an hourly rate between $41 and $52 per hour. The range displayed reflects the minimum and maximum target for new hires. Actual pay may be more or less than the posted range. Factors that influence pay include the individual's skills, qualifications, education, experience and the position level and location.
Nearest Major Market: San Jose
Nearest Secondary Market: Palo Alto
[2027 Summer] AI Research and Engineering Intern for Business Intelligence (8023)
Overview of Role
Frontier models can now reason, write code, and use tools. The next step is getting agents to work reliably over long tasks with real data, and serving them efficiently in production. Enterprise business intelligence is one of the hardest places to test this. Agents must work through messy schemas and ambiguous questions. They have to keep business logic consistent across many steps, and they have to give answers someone can check. At a company operating at TSMC's scale, wrong numbers have real costs.
AI4BI Innovation center is looking for exceptional PhD and Master's students to work on this problem with us. You will own a research and engineering question end-to-end. That means designing the method, building the agent harness and environment, training or adapting models, deploying them, and measuring progress with evaluations that hold up. The goal is a first-author paper at a top venue (e.g., NeurIPS, ICLR, ICML, ACL, EMNLP) or a prototype that ships into real decision-making workflows for business stakeholders. Many projects do both.
This is not a typical software engineering internship. You will have dedicated mentorship from AI Scientists and Data engineers, and access to realistic enterprise-scale data and business problems that are hard to get in academia.
What You'll Work On
Your project will fall within one or more of these directions. Each is grounded in real business problems.
- Agent harnesses and long-horizon agents. Design the harness around the model: the agent loop, tool and sandbox interfaces, context management and compaction, memory, sub-agent delegation, permissions, and error recovery. Build agents that can investigate a business question across hours of work. They explore data, form hypotheses, run analyses, and produce a defensible report.
- Tool use and multi-agent systems. Design agents that operate over warehouses, semantic layers, dashboards, and internal APIs through standard tool protocols such as the Model Context Protocol (MCP). Study when multi-agent orchestration helps a single strong agent, and when it gets in the way.
- Pretrained time-series foundation models. Evaluate, adapt, and fine-tune pretrained time-series foundation models (e.g., TimesFM, Chronos, Moirai) for forecasting demand, capacity, and supply-chain signals. Topics include zero-shot versus fine-tuned performance, covariates and multivariate forecasting, probabilistic forecasts and calibration, and domain-adaptive pretraining on enterprise time series. Study how LLM agents can call, combine, and explain these forecasting models, alongside optimization and network science methods.
- AI infrastructure and deployment. Build the systems that take models and agents from prototype to production. Topics include efficient LLM serving and inference optimization (batching, KV-cache management, quantization, speculative decoding), model routing and cost/latency trade-offs, on-premise and secure deployment, and observability and tracing for agent runs (including guardrails and monitoring).
- Agent environments and evaluation science. Build realistic, reproducible environments and benchmarks for data agents. Develop reliable LLM-as-judge and rubric-based evaluators. Measure what matters: correctness, consistency, calibration, latency, cost, and trustworthiness.
- Synthetic data and post-training. Generate verifiable training and evaluation tasks at scale. Adapt models to the enterprise domain through fine-tuning, distillation into small, cost-efficient models that can be deployed, and, where useful, reinforcement learning from verifiable signals such as SQL results and metric consistency checks.
Responsibilities
- Lead a research and engineering project. Frame an open problem, form hypotheses, and run careful experiments, from small-scale ablations up to training and serving on GPUs.
- Develop models and agents. Build and manage advanced machine learning, deep learning, LLM, foundation model, and time-series methods. Use them to develop data-driven solutions to complex business problems, and implement agent harnesses, tool interfaces, and environments grounded in realistic BI workloads.
- Deploy and operate. Package, serve, and monitor models and agents in production-like environments. Optimize them for reliability, latency, and cost.
- Turn data into decisions. Analyze and interpret complex data sets. Translate the results into actionable insights that improve decision-making for business stakeholders.
- Evaluate rigorously. Design benchmarks and evaluation protocols that measure reliability, not just demo quality. Report negative results honestly.
- Collaborate across teams and borders. Work with cross-functional and international teams in the US and Taiwan to find and prioritize opportunities to use data. Partner with research scientists and data engineers to develop and deploy predictive models and agentic systems. Help build and maintain efficient processes for collecting, storing, and analyzing data. Take part in weekly research syncs, paper reading groups, and brainstorming sessions.
- Communicate your work. Present insights and recommendations to both technical and non-technical stakeholders through internal reports and presentations. Write up findings for publication and/or internal deployment.
Minimum Qualifications
- Education: Currently pursuing a Ph.D. or M.S. in Computer Science, Electrical Engineering, Information Systems, Information Science, Statistics, or a related AI/ML field.
- AI-enabled analytics knowledge: Depth in one or more of machine learning, deep learning, large language models, AI agents, time-series modeling and forecasting, optimization, network science, or ML systems.
- Engineering: Strong Python skills and hands-on experience building and shipping ML or LLM systems.
- Research ability: Able to read recent papers critically, then implement and extend them quickly.
Preferred Qualifications
- Publications in leading AI conferences (e.g., NeurIPS, ICLR, ICML, ACL, KDD, MLSys) or strong pre-prints in LLMs, agents, time series, ML systems, or data management / ML for databases.
- Experience designing agent harnesses or building agents with modern agent SDKs and tool protocols (e.g., MCP), including context management, tool sandboxing, and agent tracing/observability.
- Experience with pretrained time-series foundation models (e.g., TimesFM, Chronos, Moirai) or deep forecasting methods, including fine-tuning and probabilistic evaluation.
- Experience with LLM serving and inference engines (e.g., vLLM, SGLang, TensorRT-LLM), model quantization, or inference optimization.
- Experience with MLOps and deployment infrastructure (e.g., Docker/Kubernetes, Ray, CI/CD for models, monitoring), including on-premise or GPU cluster environments.
- Hands-on LLM experience beyond prompting, such as fine-tuning, preference optimization, distillation, or RL post-training.
- Experience deploying models into user-facing tools, such as dashboarding platforms (e.g., Power BI), Python prototyping frameworks (e.g., Streamlit, Dash), and/or JavaScript frameworks (e.g., React).
- Experience with text-to-SQL, semantic parsing, tabular reasoning, forecasting, or analytics.
- Contributions to open-source AI/ML projects or public benchmarks.
Company Description
Join TSMC Technology, Inc. (TTI), an innovation hub of Taiwan Semiconductor Manufacturing Company Limited (TSMC). TSMC has been a pioneer in the semiconductor industry since 1987. With teams located in San Jose, CA, and Austin, TX, TTI is dedicated to advancing semiconductor technology through expertise in physical design, memory design, analog mixed signal, power management, and groundbreaking computational AI machine learning research. Our mission is to drive the future of semiconductor innovation through unparalleled R&D and customer-focused IP. Be part of a team that shapes the next generation of technological advancements.
As a trusted technology and capacity provider, TSMC is driven by the desire to be:
- The world’s leading dedicated semiconductor foundry
- The technology leader with a strong reputation for manufacturing excellence
- Advancing semiconductor manufacturing innovations to enable the future of technology
TSMC pioneered the pure-play foundry business model when it was founded in 1987 and has been the world’s leading dedicated semiconductor foundry ever since. The Company supports a thriving ecosystem of global customers and partners with the industry’s leading process technologies and a portfolio of design enablement solutions to unleash innovation for the global semiconductor industry. With global operations spanning Asia, Europe, and North America, TSMC serves as a committed corporate citizen around the world.
In North America, TSMC has a strong sales and service organization that works with customers by helping them achieve silicon success with cutting-edge technologies and manufacturing excellence. The Company has continued to accelerate its R&D investment and staffing in recent years and is expanding its manufacturing footprint to support customer innovation with 3D IC technologies and optimal manufacturing capacity.
For positions requiring access to technical data subject to export control regulations, including Export Administration Regulations, TSMC Technology Inc. may have to obtain export licensing approval from the U.S. Government for certain individuals. All employment is contingent upon TSMC Technology Inc. obtaining any export license or other approval that may be required by the U.S. Government.
TSMC Core Values
At TSMC, we invite talented individuals who share our vision and values to contribute to our continued success. Our core values are the foundation of our culture, guiding every decision and action. We seek applicants who deeply resonate with these principles and embody them in their work:
- Integrity: Tell the truth. We do not brag. We do not make commitments lightly. Once we make a commitment, we devote ourselves completely to meeting that commitment.
- Commitment: Employees are dedicated to the company, view the company’s success as their own and work diligently to make their best contributions. As commitment is mutual, the Company strives to serve the best interests of its employees.
- Innovation: Innovation is the wellspring of the Company's growth. It means more than new ideas; it means putting ideas into practice.
- Customer Trust: We strive to build deep and enduring relationships with our customers, who trust and rely on us to be part of their success over the long term.
(For more details on TSMC’s Core Values, please visit: https://www.tsmc.com/english/aboutTSMC/values)
Diversity statement
TSMC Technology, Inc. is committed to employing a diverse workforce and provides Equal Employment Opportunity for all individuals regardless of race, color, religion, gender, age, national origin, marital status, sexual orientation, gender identity, status as a protected veteran, genetic information, or any other characteristic protected by applicable law.
TSMC is an equal opportunity employer prizing diversity and inclusion. We are committed to treating all employees and applicants for employment with respect and dignity. If you require reasonable accommodation due to a disability during the application or the recruiting process, please feel free to notify us at g_accommodations@tsmc.com. TSMC confirms to all applicants its commitment to meet TSMC’s obligations under applicable employment law. Reasonable accommodations will be determined on a case-by-case basis.
Pay Transparency Statement
At the time of this posting, this role typically pays an hourly rate between $41 and $52 per hour. The range displayed reflects the minimum and maximum target for new hires. Actual pay may be more or less than the posted range. Factors that influence pay include the individual's skills, qualifications, education, experience and the position level and location.
Nearest Major Market: San Jose
Nearest Secondary Market: Palo Alto
Business Intelligence Associate Internship Market
Who's Hiring
Top Industries Hiring
- Banking & Financial Services
- Consulting & Professional Services
Tips for Your Business Intelligence Associate Internship Search
Apply earlier than you think necessary
Large employers open summer business intelligence associate internship applications the preceding fall, sometimes as early as August. Smaller companies and co-op programs post closer to start dates, so roles appear year-round. Check listings consistently rather than waiting for a single recruiting season, or you'll miss the first wave at the biggest programs.
Build a project portfolio before you apply
Hiring teams for business intelligence associate interns expect limited work history and assess your skills through what you've built. Document two or three complete projects showing SQL queries, dashboard designs, or data models, link them from a public repository or published report, and reference them directly on your resume so reviewers can see your work, not just your credentials.
Work your campus network and apply directly at the same time
Campus career fairs surface structured BI internship programs tied specifically to your university, and professors or career center staff often know which employers recruit from your school before roles post publicly. Applying directly to companies running smaller cohorts alongside campus activity widens your reach well beyond what either channel covers alone.
Practice the technical screen out loud before you apply
Business intelligence associate intern interviews typically include a data or analytics screen covering SQL, data modeling, or case-style questions about how you'd approach a business problem. Practice solving problems out loud and explaining your reasoning step by step, since interviewers weigh your thinking process as much as whether you reach the right answer.
Target structured BI internship programs at larger employers
Many large technology and financial services companies run cohort-based business intelligence associate internship programs designed to train candidates new to the field. These programs offer structured mentorship and project rotations, recruit earlier than general openings, and fill fast. Identify the ones that match your background and submit in the first application wave.
Set your work-type filter before you start
On-site roles are 38% of the business intelligence associate internships listed here. Decide what you can actually commit to before you start searching, then filter by location and work type on Migrate Mate so you're only reviewing roles that fit your situation rather than sorting through listings you can't take.
Business Intelligence Associate Internships: Frequently Asked Questions
How do I get a business intelligence associate internship?
Lead with coursework and personal projects rather than work history, since hiring teams expect limited experience at this level. Build a portfolio of documented analyses, dashboards, or data visualizations using tools like SQL, Python, or Tableau, and link it on your resume so recruiters have something concrete to assess. Pair direct applications with campus career fairs, where BI recruiters often move faster for students they meet in person.
Can a business intelligence associate internship turn into a full-time job?
Many employers extend return offers to strong interns, but conversion is never guaranteed. For business intelligence associate interns, what actually drives it is the quality of your project work, whether the team has open headcount when your internship ends, and how early you signal interest. Perform well on real deliverables, ask about return-offer timelines mid-internship, and treat a potential offer as a possibility rather than a certainty.
When should I apply for business intelligence associate internships?
Earlier than most expect. Large employers recruit summer cohorts the preceding fall, often opening applications in August or September. Smaller companies and co-op programs post closer to their actual start dates, so openings appear year-round. Checking listings consistently rather than waiting for a single recruiting season keeps you from missing earlier waves at larger employers.
Are business intelligence associate internships paid?
Most professional business intelligence associate internships in the U.S. are paid. Compensation varies by company size, industry, and location. Where an employer discloses pay, the listing will show it. Unpaid arrangements exist but are uncommon at established companies with structured intern programs.
What should a business intelligence associate internship resume include?
Lead with two or three complete, documented projects rather than work history. For business intelligence associate candidates, that means listing the tools used, the question you answered, and linking to your code repository, published dashboard, or analysis writeup so reviewers can assess your actual work. Add relevant coursework in databases, statistics, or data visualization, and keep the whole document to one page.
Are there remote business intelligence associate internships?
Yes. Remote and hybrid roles make up 62% of the business intelligence associate internship listings here, with the rest on-site. Remote cohorts fill fast, so apply early and use the work-type filter to surface them without sorting through roles that don't fit your situation.
Can international students get business intelligence associate internships?
Yes. F-1 students can intern through CPT while enrolled or through OPT work authorization after finishing a degree, and the employer does not have to file anything for either, so many companies are open to international interns. Confirm your eligibility and timing with your university's international student office before accepting an offer.
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