Data Science Manager Internships
Data science manager internships give university students, recent graduates, and early-career switchers hands-on project experience leading real analytical work, mentorship from working data science managers, and, at many employers, a path toward a full-time offer. Openings are concentrated across Technology & Software and Distribution & Wholesale, with TikTok, BNY, and Lyft among the employers posting roles now.
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About Ancestry:
When you join Ancestry, you join a human-centered company where every person’s story is important. Ancestry®, the global leader in family history, connects everyone with their past so they can discover, preserve, and share their unique family stories. With our unparalleled collection of more than 65 billion records, over 3.5 million subscribers, and over 27 million people in our growing DNA network, customers can discover their family story and gain a new level of understanding about their lives. Over the past 40 years, we’ve built trusted relationships with millions of people who have chosen us as the platform for discovering, preserving, and sharing the most important information about themselves and their families.
We are committed to our location flexible work approach, allowing you to choose to work in the nearest office, from your home, or a hybrid of both (subject to location restrictions and roles that are required to be in the office- ). We will continue to hire and promote beyond the boundaries of our office locations, to enable broadened possibilities for employee diversity.
Together, we work every day to foster a work environment that's inclusive as well as diverse, and where our people can be themselves. Every idea and perspective is valued so that our products and services reflect the global and diverse clients we serve.
Ancestry encourages applications from minorities, women, the disabled, protected veterans and all other qualified applicants. Passionate about dedicating your work to enriching people’s lives? Join the curious.
Ancestry is seeking an exceptional and highly motivated Data Science Co-Op to join our Content AI team, a dynamic group at the forefront of Document Understanding. You’ll play a vital role in developing innovative AI models that extract and organize text and image information from billions of historical and genealogical records enabling customers to discover, share, and connect with their family history. As a Co-Op on the Content AI team, you will build, train and fine-tune models that process historical documents to detect meaningful, personalized insights within historical documents that connect people to their ancestors. You will also work closely with engineering teams to train, optimize, and deploy models that promote product development, customer success, and content creation across our Family History business.
What you will do:
Innovate with State-of-the-Art AI: Implement and experiment with cutting-edge transformer and generative AI solutions for key Document Understanding tasks, including OCR, handwriting recognition, transcription, Named Entity Recognition (NER), Relation Extraction (RE), Coreference Resolution, Summarization, and Knowledge Graphs working with diverse genealogical and historical collections spanning newspapers, city directories, family history books, and vital records (birth, marriage, death).
Analyze and Optimize Multi-Modal Models: Evaluate the performance of multi-modal models in zero-shot and few-shot learning scenarios for comprehensive document understanding.
Collaborate on Cloud Deployment: Partner closely with ML Ops and Data Science Engineers to seamlessly deploy datasets, truth sets, models, and pipelines for training and inference in cloud environments.
Communicate Insights Effectively: Clearly and confidently present your findings, deliverables, and proposed solutions to technical and non-technical audiences, including teams, stakeholders, and executives.
Who You Are:
Currently pursuing an advanced degree (Master's or PhD preferred) in Computer Science, Data Science, Statistics, Mathematics, Linguistics, Engineering or related quantitative field with a strong data focus.
Specialization in generative AI & LLMs, embeddings, LoRA, QLoRA, vector databases, transformer models, Natural Language Processing (NLP), with software development expertise including data structures, distributed model training, and inference optimizations.
Exhibit strong proficiency in Python and relevant tools and libraries, including those for transformer models, multi-modal models, and general NLP (e.g., Hugging Face Transformers, agentic frameworks and workflows, LangChain, LangGraph, NLTK).
Familiarity with cloud platforms and related AI/ML services such as Google Gemini API, Vertex AI, AWS EC2, S3, SageMaker, Model Registry, and Bedrock is a plus.
Additional Information:
Ancestry is an Equal Opportunity Employer that makes employment decisions without regard to race, color, religious creed, national origin, ancestry, sex, pregnancy, sexual orientation, gender, gender identity, gender expression, age, mental or physical disability, medical condition, military or veteran status, citizenship, marital status, genetic information, or any other characteristic protected by applicable law. In addition, Ancestry will provide reasonable accommodations for qualified individuals with disabilities.
All job offers are contingent on a background check screen that complies with applicable law. For candidates who live in San Francisco, CA, pursuant to the San Francisco Fair Chance Ordinance, Ancestry will consider for employment qualified applicants with arrest and conviction records.
Ancestry is not accepting unsolicited assistance from search firms for this employment opportunity. All resumes submitted by search firms to any employee at Ancestry via-email, the Internet or in any form and/or method without a valid written search agreement in place for this position will be deemed the sole property of Ancestry. No fee will be paid in the event the candidate is hired by Ancestry as a result of the referral or through other means.
About Ancestry:
When you join Ancestry, you join a human-centered company where every person’s story is important. Ancestry®, the global leader in family history, connects everyone with their past so they can discover, preserve, and share their unique family stories. With our unparalleled collection of more than 65 billion records, over 3.5 million subscribers, and over 27 million people in our growing DNA network, customers can discover their family story and gain a new level of understanding about their lives. Over the past 40 years, we’ve built trusted relationships with millions of people who have chosen us as the platform for discovering, preserving, and sharing the most important information about themselves and their families.
We are committed to our location flexible work approach, allowing you to choose to work in the nearest office, from your home, or a hybrid of both (subject to location restrictions and roles that are required to be in the office- ). We will continue to hire and promote beyond the boundaries of our office locations, to enable broadened possibilities for employee diversity.
Together, we work every day to foster a work environment that's inclusive as well as diverse, and where our people can be themselves. Every idea and perspective is valued so that our products and services reflect the global and diverse clients we serve.
Ancestry encourages applications from minorities, women, the disabled, protected veterans and all other qualified applicants. Passionate about dedicating your work to enriching people’s lives? Join the curious.
Ancestry is seeking an exceptional and highly motivated Data Science Co-Op to join our Content AI team, a dynamic group at the forefront of Document Understanding. You’ll play a vital role in developing innovative AI models that extract and organize text and image information from billions of historical and genealogical records enabling customers to discover, share, and connect with their family history. As a Co-Op on the Content AI team, you will build, train and fine-tune models that process historical documents to detect meaningful, personalized insights within historical documents that connect people to their ancestors. You will also work closely with engineering teams to train, optimize, and deploy models that promote product development, customer success, and content creation across our Family History business.
What you will do:
Innovate with State-of-the-Art AI: Implement and experiment with cutting-edge transformer and generative AI solutions for key Document Understanding tasks, including OCR, handwriting recognition, transcription, Named Entity Recognition (NER), Relation Extraction (RE), Coreference Resolution, Summarization, and Knowledge Graphs working with diverse genealogical and historical collections spanning newspapers, city directories, family history books, and vital records (birth, marriage, death).
Analyze and Optimize Multi-Modal Models: Evaluate the performance of multi-modal models in zero-shot and few-shot learning scenarios for comprehensive document understanding.
Collaborate on Cloud Deployment: Partner closely with ML Ops and Data Science Engineers to seamlessly deploy datasets, truth sets, models, and pipelines for training and inference in cloud environments.
Communicate Insights Effectively: Clearly and confidently present your findings, deliverables, and proposed solutions to technical and non-technical audiences, including teams, stakeholders, and executives.
Who You Are:
Currently pursuing an advanced degree (Master's or PhD preferred) in Computer Science, Data Science, Statistics, Mathematics, Linguistics, Engineering or related quantitative field with a strong data focus.
Specialization in generative AI & LLMs, embeddings, LoRA, QLoRA, vector databases, transformer models, Natural Language Processing (NLP), with software development expertise including data structures, distributed model training, and inference optimizations.
Exhibit strong proficiency in Python and relevant tools and libraries, including those for transformer models, multi-modal models, and general NLP (e.g., Hugging Face Transformers, agentic frameworks and workflows, LangChain, LangGraph, NLTK).
Familiarity with cloud platforms and related AI/ML services such as Google Gemini API, Vertex AI, AWS EC2, S3, SageMaker, Model Registry, and Bedrock is a plus.
Additional Information:
Ancestry is an Equal Opportunity Employer that makes employment decisions without regard to race, color, religious creed, national origin, ancestry, sex, pregnancy, sexual orientation, gender, gender identity, gender expression, age, mental or physical disability, medical condition, military or veteran status, citizenship, marital status, genetic information, or any other characteristic protected by applicable law. In addition, Ancestry will provide reasonable accommodations for qualified individuals with disabilities.
All job offers are contingent on a background check screen that complies with applicable law. For candidates who live in San Francisco, CA, pursuant to the San Francisco Fair Chance Ordinance, Ancestry will consider for employment qualified applicants with arrest and conviction records.
Ancestry is not accepting unsolicited assistance from search firms for this employment opportunity. All resumes submitted by search firms to any employee at Ancestry via-email, the Internet or in any form and/or method without a valid written search agreement in place for this position will be deemed the sole property of Ancestry. No fee will be paid in the event the candidate is hired by Ancestry as a result of the referral or through other means.
Data Science Manager Internship Market
Who's Hiring
Top Industries Hiring
- Technology & Software
- Distribution & Wholesale
Tips for Your Data Science Manager Internship Search
Apply in fall for summer data science manager roles
Large employers open summer internship applications the preceding fall, sometimes months before the program starts. Smaller companies and co-op programs post much closer to their start dates. Check new listings regularly so you catch structured programs in the first application wave rather than after they close.
Build a portfolio before you send your first application
Data science manager intern candidates are assessed on documented project work, not work history. Put together two or three end-to-end projects that show data wrangling, modeling, and communication of results, link your code, published notebooks, or analysis write-ups so recruiters can review the actual work, not just a bullet on a resume.
Work your campus network alongside direct applications
Campus career fairs surface structured internship programs tied to your university, and recruiters there often move faster for students they meet in person. Your professors and career center staff frequently know which data science employers recruit from your school before roles post publicly. Pair that network with direct applications to companies running smaller cohorts to widen your reach.
Practice your technical screen out loud before interviewing
Data science manager intern screens typically involve a technical or analytics component, expect questions on statistics, SQL, Python, or model evaluation depending on the team. Practice talking through your reasoning as you work, not just reaching the answer, since interviewers weigh how you think as much as the final output. Record yourself and review it.
Target structured rotational and cohort programs early
Many larger employers run formal data science rotational or cohort programs built to develop people new to the manager track. These programs recruit early, fill fast, and often have dedicated campus timelines separate from general hiring. Identify the ones that fit your background and get your application in during the first wave.
Set your work-type filter before you start searching
On-site roles are 34% of the data science manager internships listed here. Decide what you can realistically commit to before you start reviewing openings, then filter by location and work type so you focus only on roles you can actually take. Migrate Mate's work-type filter lets you narrow the feed before you read a single listing.
Data Science Manager Internships: Frequently Asked Questions
How do I get a data science manager internship?
Lead with coursework and personal projects rather than work history, hiring teams expect limited experience at the intern level. Build a portfolio of documented analyses or model-building projects that recruiters can actually review. Apply directly to companies online and attend campus career fairs, where data science recruiters often move faster for students they meet in person.
Can a data science manager internship turn into a full-time job?
Many employers extend return offers to strong interns, but conversion is never guaranteed. What drives it for data science manager interns is performance on real project deliverables, available headcount on the team, and the timing of the return-offer window. Treat the internship as a long audition, deliver results and ask your manager early about the process without counting on an offer.
When should I apply for data science manager internships?
Earlier than most expect. Large employers open summer internship applications the preceding fall, sometimes as early as August or September. Smaller companies and co-op programs post closer to actual start dates, so openings appear year-round. Check postings regularly and apply as soon as a role goes live rather than waiting until the deadline.
Are data science manager internships paid?
Most professional data science manager internships in the U.S. are paid. Compensation varies by company size, industry, and location. Where an employer discloses pay, it appears directly in the listing, search the openings here and filter by the details that matter to you.
What should a data science manager internship resume include?
Lead with two or three complete, documented projects that show your analytical and leadership thinking, include the tools you used and link to the work directly, whether that is a GitHub repository, a published analysis, or a case study write-up. Add relevant coursework below your projects, keep the whole document to one page, and put work history last if you have any.
Are there remote data science manager internships?
Yes. Remote and hybrid roles make up 66% of the data science manager internship listings here, with the rest on-site. Remote cohorts fill fast, so apply early and filter by work type to see them before they close.
Can international students get data science manager 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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