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Theoretical Computer Science Intern Jobs in Cumberland, RI

FocusKPI Inc.

Data Science Intern

Boston, MA · Remote

Data Science Intern Statistical Modeling & Marketing Measurement Remote | Internship | Full-time | ... Currently pursuing a degree in statistics, computer science, data science, machine learning ...

New

FocusKPI Inc.

Data Science Intern

Boston, MA · On-site +1

Data Science Intern Statistical Modeling & Marketing Measurement Remote | Internship | Full-time | ... Currently pursuing a degree in statistics, computer science, data science, machine learning ...

New

Varsity Tutors

College Computer Science Tutor

Boston, MA · Remote

$18 - $40/hr

Emphasizes theoretical foundations alongside practical implementation and connects computer science to artificial intelligence, distributed systems, and industry engineering practices. * Curriculum ...

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Theoretical Computer Science Intern information

What does a theoretical computer science intern do?

A Theoretical Computer Science Intern typically works on fundamental problems in computer science, such as algorithms, computational complexity, cryptography, or data structures. Their work often involves mathematical proofs, designing algorithms, and analyzing their efficiency rather than practical software development. Interns may assist with ongoing research projects, collaborate with senior researchers, and contribute to academic papers or presentations. The goal is to deepen understanding of the theoretical foundations that underpin computer technology.

What types of projects or research topics does a theoretical computer science intern typically work on during their internship?

As a Theoretical Computer Science Intern, you'll often contribute to projects involving algorithm design, computational complexity, cryptography, or formal verification. Interns usually work closely with research scientists or professors, assisting in literature reviews, developing mathematical proofs, and running computational experiments. Collaboration is key, and you may present findings in group meetings or co-author papers. These internships provide an excellent opportunity to deepen your theoretical knowledge while gaining practical experience in a collaborative research environment.

What are the key skills and qualifications needed to thrive as a theoretical computer science intern, and why are they important?

To thrive as a Theoretical Computer Science Intern, you need a solid background in discrete mathematics, algorithms, and computational theory, often supported by ongoing or completed coursework in computer science or mathematics. Familiarity with programming languages like Python or C++, and tools such as LaTeX for documentation, is commonly required. Strong analytical thinking, problem-solving abilities, and effective communication skills help you stand out in collaborative research environments. These skills are crucial for tackling complex theoretical problems, contributing to research projects, and clearly presenting findings.

What is the difference between Theoretical Computer Science Intern vs Software Development Intern?

AspectTheoretical Computer Science InternSoftware Development Intern
Required CredentialsComputer science coursework, strong math skillsProgramming skills, coursework in software engineering
Work EnvironmentResearch labs, academic settings, tech companiesDevelopment teams, tech companies, startups
Industry UsageResearch projects, algorithm development, academiaApplication development, product building, coding

Theoretical Computer Science Interns focus on research, algorithms, and mathematical foundations, often in academic or research settings. Software Development Interns work on coding, building applications, and software projects in industry environments. Both roles require strong technical skills but differ in their focus and work environment.

Theoretical Computer Science Intern jobs near you

Data Science Intern

FocusKPI Inc.

Boston, MA • Remote

Other

Posted yesterday

New

Job description

Data Science Intern
Statistical Modeling & Marketing Measurement
Remote  |  Internship  |  Full-time  |  3 months
About the Role
We are looking for a curious and analytically minded Data Science Intern to support the development and evaluation of statistical and machine learning models for marketing measurement. This role is designed for someone with strong quantitative fundamentals who wants hands-on experience applying regression, model diagnostics, validation, and data analysis to real business problems. You will work closely with experienced data scientists, learn how modeling choices affect interpretation and business decisions, and contribute clean, reproducible analytical work.
Responsibilities
  • Support the development and evaluation of models including regression, time-series, and other statistical or machine learning approaches, with attention to predictive performance, stability, and interpretability.
  • Prepare and explore data using Python and SQL; perform data-quality checks, feature construction, descriptive analysis, and visualization to understand modeling inputs and outcomes.
  • Apply core model-validation techniques such as train/validation/test splits, cross-validation, baseline comparisons, and appropriate performance metrics.
  • Investigate common statistical issues including multicollinearity, overfitting, residual patterns, autocorrelation, heteroskedasticity, and unstable coefficients, with guidance from senior team members.
  • Test and compare reasonable modeling choices such as feature transformations, regularization settings, and model specifications, and summarize how these choices affect model results.
  • Interpret model outputs and connect technical findings to practical marketing or business questions while clearly stating assumptions and limitations.
  • Contribute to reproducible analytical workflows for model training, validation, sensitivity checks, and result comparison.
  • Write clear Python and SQL code and communicate methods, findings, assumptions, and open questions in a structured and understandable way.

Basic Qualifications
  • Strong foundation in statistics and regression: understanding of linear regression, key model assumptions, coefficient interpretation, regularization concepts, and basic statistical inference.
  • Solid quantitative fundamentals in probability, statistics, and linear algebra; familiarity with calculus or optimization concepts is helpful.
  • Working knowledge of Python for data analysis and modeling, including common data-science libraries; basic to intermediate SQL skills for data extraction and transformation.
  • Understanding of model evaluation: training versus validation data, cross-validation, common regression metrics, overfitting, and the importance of out-of-sample performance.
  • Ability to reason through modeling problems: investigate unexpected results, form hypotheses about root causes, test alternatives, and explain conclusions using evidence.
  • Clear communication skills: ability to explain analytical methods, assumptions, results, and limitations to technical teammates and learn from feedback.
  • Currently pursuing a degree in statistics, computer science, data science, machine learning, applied mathematics, econometrics, operations research, or a closely related quantitative field.

Preferred Qualifications
  • Coursework, research, or project experience using regression, time-series analysis, statistical modeling, or machine learning.
  • Exposure to Marketing Mix Modeling (MMM), marketing analytics, attribution, or other measurement problems.
  • Basic understanding of concepts such as adstock, saturation, incremental impact, ROI, or response curves.
  • Familiarity with A/B testing, causal inference, simulation, sensitivity analysis, or confidence intervals.
  • Experience with Python libraries such as pandas, NumPy, statsmodels, scikit-learn, SciPy, or similar tools.
  • Previous internship, research assistantship, academic project, or independent project involving real-world data is a plus.

NOTICE: Please be aware of fraudulent emails regarding job postings, job offers and fake checks. FocusKPI's recruiting team will strictly reach out via @focuskpi.com email domain. If you have received fraudulent emails now or in the past, please report it to https://reportfraud.ftc.gov/ .
The domain @focuskpijobs.com is fraudulent and not related to FocusKPI. Please do not not reply or communicate to anyone with @focuskpijobs.com.

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About FocusKPI

Sourced by ZipRecruiter

Industry

Computing infrastructure providers, data processing, web hosting

Company size

51 - 200 Employees

Headquarters location

Santa Clara, CA, US

Year founded

2010

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