... causal inference. Search & Discovery ML : The Search and Discovery ML team at Instacart works ... Past internship contributions include: Tensor-based complementary recommendations, published at ...
... causal inference. Search & Discovery ML : The Search and Discovery ML team at Instacart works ... Past internship contributions include: Tensor-based complementary recommendations, published at ...
... causal inference. Search & Discovery ML : The Search and Discovery ML team at Instacart works ... Past internship contributions include: Tensor-based complementary recommendations, published at ...
... causal inference. Search & Discovery ML : The Search and Discovery ML team at Instacart works ... Past internship contributions include: Tensor-based complementary recommendations, published at ...
... causal inference. Search & Discovery ML : The Search and Discovery ML team at Instacart works ... Past internship contributions include: Tensor-based complementary recommendations, published at ...
... causal inference. Search & Discovery ML : The Search and Discovery ML team at Instacart works ... Past internship contributions include: Tensor-based complementary recommendations, published at ...
... causal inference. Search & Discovery ML : The Search and Discovery ML team at Instacart works ... Past internship contributions include: Tensor-based complementary recommendations, published at ...
... causal inference. Search & Discovery ML : The Search and Discovery ML team at Instacart works ... Past internship contributions include: Tensor-based complementary recommendations, published at ...
... causal inference. Search & Discovery ML : The Search and Discovery ML team at Instacart works ... Past internship contributions include: Tensor-based complementary recommendations, published at ...
... causal inference. Search & Discovery ML : The Search and Discovery ML team at Instacart works ... Past internship contributions include: Tensor-based complementary recommendations, published at ...
Internship Causal Inference information
What are the key skills and qualifications needed to thrive as an Internship Causal Inference, and why are they important?
To thrive in an Internship Causal Inference role, you need a solid background in statistics, econometrics, and data analysis, typically supported by coursework or degrees in statistics, economics, or related quantitative fields. Familiarity with statistical programming languages such as R or Python, and experience with causal inference frameworks and tools like propensity score matching or regression discontinuity, are commonly required. Strong problem-solving abilities, attention to detail, and effective communication skills help interns interpret results and collaborate with research teams. These skills and qualities are essential to ensure rigorous and meaningful analysis that informs data-driven decisions.
What types of projects and team collaborations can I expect during an Internship in Causal Inference?
As an intern in Causal Inference, you will typically work on projects focused on analyzing data to determine cause-and-effect relationships, such as assessing the impact of interventions or policy changes. You may collaborate with data scientists, statisticians, and domain experts, contributing to experimental design, data cleaning, and the application of statistical methods. Interns often participate in weekly team meetings, present findings, and receive mentorship from senior researchers. This hands-on experience provides valuable exposure to both technical skills and interdisciplinary teamwork, which are crucial for growth in quantitative research roles.
What is an Internship in Causal Inference?
An Internship in Causal Inference is a temporary position, typically for students or early-career professionals, that focuses on learning and applying methods to determine cause-and-effect relationships in data. Interns in this field work with statistical models, experimental designs, and software tools to analyze data and infer causal relationships, often in fields like economics, public health, or data science. These internships provide hands-on experience with real-world datasets, mentorship from experienced researchers, and opportunities to contribute to ongoing projects. Participants gain valuable skills in programming, statistical analysis, and research methodology, which are highly sought after in both academia and industry.
What is the difference between Internship Causal Inference vs Data Analyst?
| Aspect | Internship Causal Inference | Data Analyst |
|---|---|---|
| Required Credentials | Undergraduate or graduate in statistics, economics, or related fields | Degree in statistics, data science, or related fields |
| Work Environment | Research-focused, often in academia or research institutions | Business, corporate, or consulting settings |
| Employer & Industry Usage | Universities, research labs, tech companies | Finance, marketing, healthcare, tech companies |
| Comparison Search Intent | Understanding causal inference techniques during internships | Analyzing data to inform business decisions |
Internship Causal Inference roles focus on applying statistical methods to identify cause-effect relationships, often in research settings. Data Analyst roles involve interpreting data to support business strategies. While both require analytical skills, causal inference internships emphasize research and advanced statistical techniques, whereas data analyst positions focus on data processing and reporting.
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What cities in Rhode Island are hiring for Internship Causal Inference jobs? Cities in Rhode Island with the most Internship Causal Inference job openings:
Instacart rating
6.7
Based on 29 frontline employees who took The Breakroom Quiz
Job description
We're transforming the grocery industry
At Instacart, we invite the world to share love through food because we believe everyone should have access to the food they love and more time to enjoy it together. Where others see a simple need for grocery delivery, we see exciting complexity and endless opportunity to serve the varied needs of our community. We work to deliver an essential service that customers rely on to get their groceries and household goods, while also offering safe and flexible earnings opportunities to Instacart Personal Shoppers.
Instacart has become a lifeline for millions of people, and we’re building the team to help push our shopping cart forward. If you’re ready to do the best work of your life, come join our table.
Instacart is a Flex First team
There’s no one-size fits all approach to how we do our best work. Our employees have the flexibility to choose where they do their best work—whether it’s from home, an office, or your favorite coffee shop—while staying connected and building community through regular in-person events.
Overview
Since 2012, Instacart has been focused on making grocery delivery convenient, affordable, and accessible to everyone. We bring fresh groceries and everyday essentials to customers across the US and Canada from nearly 55,000 stores across 5,500 markets. Our mission is to create a world where everyone has access to the food they love, and to achieve that goal, we innovate in a wide range of areas including e-commerce, advertising, and fulfillment.
We use machine learning and Internet-scale data to elevate customer experience, improve efficiency, and reduce cost. As an example, we manage catalog data imported from hundreds of retailers, and we build product and knowledge graphs on top of the catalog data to support a wide range of applications including search and ads.
We are looking for talented Ph.D. students to have an internship in our fast moving team. You will have the opportunity to work on a very large scope of problems in search, ads, personalization, recommendation, fulfillment, product and knowledge graph, pricing, etc.
About the Team:
This is a general posting for multiple intern roles open across our various ML teams. You can find a blurb on each team below:
Economics Team: The Economics team at Instacart works on a range of interesting and challenging problems, from aligning the incentives in our multi-sided marketplace to analyzing the role of prices and product placement in our customers' decision-making. Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference.
Search & Discovery ML: The Search and Discovery ML team at Instacart works alongside world-class engineers, data scientists, and product managers to shape the future of search technology at Instacart. They collaborate on building models that enhance relevance of all shopping surfaces, ranking, and personalization, delivering highly relevant results to users across the Instacart ecosystem. As part of the Search and Discovery ML team, you'll work on one of the most critical aspects of the business, helping customers connect with the right products. We are passionate about solving large-scale search challenges and creating innovative solutions that elevate the customer experience. (Recent publications 1, 2, 3, 4, 5).
Content AI Team: The Content AI team at Instacart works alongside world-class engineers, data scientists, and product managers to advance generative AI, recommendations, and catalog intelligence in grocery ecommerce. We build cutting-edge AI models that power real-time recommendations, feed ranking, and automated content generation, ensuring high-quality and engaging customer experiences. Beyond recommendations, we leverage generative AI and LLMs to enhance and enrich Instacart’s catalog, driving AI-powered product understanding and content creation at scale. As part of Content AI, you'll work on high-impact AI solutions, applying LLMs, agentic systems, and computer vision to tackle complex challenges. We are passionate about pushing the boundaries of generative AI to shape the future of ecommerce. If you're excited about building state-of-the-art AI systems, we’d love to have you on board!
Past internship contributions include:
Tensor-based complementary recommendations, published at IEEE Big Data 2021 (Paper) Enhancing sequence-based recommendations for long-tail products (Blog)
About the Job
Based on your passion and background, you may choose to work in a few different areas:
- Query understanding - Using cutting-edge NLP technologies to understand the intent of user queries.
- Search relevance and ranking - Improving search relevance by incorporating signals from various sources.
- Ads quality, pCTR, etc. - Improving ads revenue and ROAS.
- Knowledge graphs - Working on graph data management and knowledge discovery, and creating a natural language interface for data access.
- Fraud detection and prevention - Using cost sensitive learning to reduce loss.
- Pricing - Estimating willingness-to-pay, and optimizing revenue and user experience.
- Logistics - Optimization in a variety of situations, including supply/demand prediction, last mile delivery, in-store optimization, etc.
About You
Minimum Qualifications:
- Ph.D. student in computer science, mathematics, statistics, economics, or related areas.
- Strong programming (Python, C++) and algorithmic skills.
- Good communication skills. Curious, willing to learn, self-motivated, hands-on.
Preferred Qualifications:
- Ph.D. student at a top tier university in the United States
- Prior internship/work experience in the machine learning space
Instacart provides highly market-competitive compensation and benefits in each location where our employees work. This role is remote and the base pay range for a successful candidate is dependent on their permanent work location. Please review our Flex First remote work policy here.
Offers may vary based on many factors, such as candidate experience and skills required for the role.
What Instacart employees say
Pay
Benefits
Hours and flexibility
Workplace
Get the full story on Breakroom
About Instacart
Sourced by ZipRecruiter
Instacart, based in San Francisco, CA, US, operates within the retail industry, specifically grocery delivery and pick-up service. It is recognized as a pioneer in this field, delivering fresh groceries from local stores directly to customers' doors. The company, which launched its services in 2012, continues to pioneer change in the online grocery shopping sector through its commitment to cutting-edge technology, new business ideas, and dedicated service.
Industry
Technology, communication and media
Company size
10,000+ Employees
Headquarters location
San Francisco, CA, US
Year founded
2012