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Internship Data Science R Jobs in New York, NY (NOW HIRING)

Cigna

Data Science Advisor - Hybrid

Morris Plains, NJ · On-site +1

As a Data Science Advisor, you will apply advanced analytics, machine learning, and AI to deliver ... Strong programming skills in Python, R, SQL, or similar tools. * Hands-on experience with machine ...

Atrosphere Technologies

Data Scientist

Manhattan, NY · On-site

$80 - $120/hr

Master's degree in Data Science, Statistics, or related field * 3+ years of experience in data science or analytics * Proficiency in Python, R, SQL, and data visualization tools * Experience with ...

New

American Express

Directors, Data Science

Manhattan, NY · On-site

$169K - $215K/yr

American Express Company seeks Directors, Data Science to drive the strategic analysis of credit ... R; linear regression models, logistic regression models, and advanced machine learning models ...

PVH

Manager, Data Science- PVH Corp.

New York, NY · On-site

This role owns the modeling strategy, data science roadmap, and decisionsupport frameworks that ... Build scalable data pipelines and modeling workflows using SQL, Python/R, Spark, and cloudbased or ...

Brillio

Principal Data Scientist - R01568403

Edison, NJ · On-site

$180K - $190K/yr

Experience Range: 15 - 18 years of experience in advanced data science roles, with extensive ... and R, ensuring efficient data processing and feature engineering 5. Develop, validate, and ...

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Internship Data Science R information

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How much do internship data science r jobs pay per hour?

As of Aug 8, 2026, the average hourly pay for internship data science r in New York, NY is $24.62, according to ZipRecruiter salary data. Most workers in this role earn between $18.94 and $26.83 per hour, depending on experience, location, and employer.

What types of projects can I expect to work on during an internship data science R?

As a Data Science intern at R, you will typically be involved in projects such as data cleaning, exploratory data analysis, and building predictive models under the guidance of experienced data scientists. You may also contribute to developing data visualizations and presenting insights to stakeholders. Interns often collaborate with cross-functional teams, including software engineers and business analysts, which provides valuable exposure to real-world data challenges and team-based problem solving.

What is the difference between Internship Data Science R vs Data Analyst Intern?

AspectInternship Data Science RData Analyst Intern
Required SkillsProficiency in R, statistical analysis, data visualizationExcel, SQL, basic statistical knowledge
Work EnvironmentData science teams, research projects, analytics departmentsBusiness units, marketing, finance, or operations teams
Industry UsageTech, finance, healthcare, research institutionsRetail, marketing, consulting, finance

Internship Data Science R focuses on applying R programming for statistical analysis and data modeling, often in research or technical environments. Data Analyst Internships emphasize data cleaning, visualization, and reporting using tools like Excel and SQL. Both roles require analytical skills but differ in technical depth and industry focus.

What is an internship data science R?

An Internship Data Science R is a temporary position for students or recent graduates to gain practical experience in data science, with a focus on using the R programming language. Interns typically work under the guidance of experienced data scientists, assisting with data cleaning, analysis, visualization, and possibly building statistical models. This role helps interns develop technical and analytical skills, and provides exposure to real-world data-driven projects, often found in industries like finance, healthcare, or technology.

What are the key skills and qualifications needed to thrive as an internship data science R, and why are they important?

To thrive as an Internship Data Science R, you need a solid grounding in statistics, data analysis, and programming with R, typically supported by coursework or a degree in a quantitative field. Familiarity with R packages (like tidyverse, ggplot2), data visualization tools, and version control systems such as Git is often required. Strong problem-solving skills, attention to detail, and effective communication help interns translate data insights into actionable recommendations. These abilities are crucial for supporting data-driven decision-making and contributing meaningfully to project teams in a professional environment.
What are the most commonly searched types of Data Science R jobs in New York, NY? The most popular types of Data Science R jobs in New York, NY are:
What cities near New York, NY are hiring for Internship Data Science R jobs? Cities near New York, NY with the most Internship Data Science R job openings:
Internship Data Science R jobs near you

Data Science Machine Learning Internship (Summer 2027)

Castleton Commodities International LLC

Stamford, CT

Full-time

Posted 17 days ago

Job description

Application Deadline: September 1, 11:59pm EST

Program Summary - Commercial Technology Internships

Company Overview:

Castleton Commodities International is a leading global energy commodities merchant and infrastructure asset investor. As a trader, CCI deploys capital on a proprietary basis in the physical and financial commodity markets, providing the Company with market insights and access. As a strategic investor and developer, CCI leverages its market expertise, operations capabilities, and industry knowledge to invest in, and develop, select commodity infrastructure assets. Our strategically integrated platform has generated strong risk-adjusted returns for our investors since our formation.

Position Overview:

CCI is developing a leading-edge Data Science platform, as staying at the forefront of data management and analytics is essential to our investment strategy. We are looking for motivated and detail-oriented Machine Learning Interns with a strong interest in quantitative analysis, particularly time series forecasting to join our Global Data Science team in Stamford, CT, Houston, TX, or New York City offices. Our Machine Learning Internship provides a unique opportunity to work with fundamental market data, generating insights that support our commercial trading business. You will be responsible for analyzing time series data related to market fundamentals in the Power, Natural Gas, and Oil sectors, helping to identify key supply and demand drivers. These insights will play a vital role in forecasting price movements and supporting risk management decisions.

Responsibilities:

  • Apply mathematical and statistical knowledge to enhance existing machine learning applications and explore new solutions.
  • Work closely with Data Scientists, Analysts, and Traders to design, implement, and optimize machine learning models for time series forecasting, including ARIMA/SARIMA, gradient boosting methods (e.g., XGBoost), LSTM networks, and linear regression-based approaches.
  • Assist in designing and implementing end-to-end data ingestion processes, ensuring seamless data flow to investing teams.
  • Work with desk heads, traders, and analysts to understand current data architecture, investment processes, and functional requirements for data science analysis.
  • Contribute to identifying and back-testing new data sets, leveraging machine learning techniques to drive insights.
  • Conduct ad hoc research on emerging project topics, including energy fundamental data, analytics trends, and best practices in big data and artificial intelligence.

Qualifications:

  • Currently pursuing a Bachelor's Degree or higher in Mathematics, Statistics, Physics, Computer Science or related technical field with a focus in Machine Learning.
  • Expected graduation date of Winter 2027 or Spring/Summer 2028.
  • Experience applying machine learning techniques such as regression, time series forecasting, deep learning, reinforcement learning, or predictive modeling to solve problems involving complex data patterns and market dynamics.
  • Strong programming experience in Python (preferred libraries: Pandas, NumPy, etc.)
  • Ability to communicate and interact with a wide range of users, from very technical to non-technical backgrounds.
  • Strong analytical skills with demonstrated attention to detail.

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