Computer Science Intern Jobs in Philadelphia, PA

Since 1869 we've connected people through food they love. We?re proud to be stewards of amazing brands that people trust. Our portfolio includes the iconic Campbell?s brand, as well as Michael Angelo?s, Pace, Pacific Foods, Prego, Rao?s Homemade, Swanson, and V8. In our Snacks division, we have brands like Cape Cod, Goldfish, Kettle Brand, Lance, Late July, Pepperidge Farm, Snack Factory, and Snyder?s of Hanover. Here, you will make a difference every day. You will be supported to build a rewarding career with opportunities to grow, innovate and inspire. Make history with us. Why Campbell?s Benefits begin on day one and include medical, dental, short and long-term disability, AD&D, and life insurance (for individual, families, and domestic partners). Employees are eligible for our matching 401(k) plan and can enroll on the first day of employment with immediate vesting. Campbell?s offers unlimited sick time along with paid time off and holiday pay. If in WHQ free access to the fitness center. Access to on-site day care (operated by Bright Horizons) and company store. Giving back to the communities where our employees work and live is very important to Campbell?s. Our Campbell?s Cares program matches employee donations and/or volunteer activity up to $1,500 annually. Campbell?s has a variety of Employee Resource Groups (ERGs) to support employees.The Data Science Intern will support the Data Science team in developing and applying statistical and machine learning models to generate actionable insights that drive value across Campbell?s business. This internship offers hands-on experience in time series forecasting, statistical analysis, and predictive modeling within a dynamic CPG environment. The ideal candidate has a strong quantitative foundation, a passion for solving real-world problems with data, and the ability to communicate analytical findings clearly to business stakeholders.Essential ResponsibilitiesResponsibilities will include but not be limited to:Develop and refine time series forecasting models (e.g., ARIMA, Prophet, exponential smoothing, XGBoost) to support demand planning and supply chain decision-making.Apply statistical and machine learning techniques including regression, classification, clustering, and hypothesis testing to analyze business data and generate actionable recommendations.Conduct exploratory data analysis and statistical testing to identify trends, patterns, and opportunities across product segments and markets.Retrieve, cleanse, transform, and analyze complex datasets from multiple sources to support ongoing analytics initiatives.Create clear and compelling data visualizations to communicate findings and drive stakeholder buy-in.Collaborate with cross-functional teams (Supply Chain, Finance, Marketing, Sales) to identify areas where data science can deliver measurable business impact.Support data engineers in evaluating and improving the existing data science infrastructure, tools, and pipelines.Document analytical methodologies, assumptions, and results to ensure reproducibility and knowledge sharing.RequirementsCurrently pursuing an MS or Ph.D. in Statistics, Data Science, Computer Science, Mathematics, Engineering, Operations Research, or a related quantitative field. Ph.D. candidates are strongly preferred.Strong foundation in time series analysis and forecasting methods (e.g., ARIMA, SARIMA, exponential smoothing, state-space models, Prophet, or tree-based approaches).Solid knowledge of statistical modeling and machine learning, including regression, classification, clustering, dimensionality reduction, and model evaluation techniques.Demonstrated experience with statistical analysis: hypothesis testing, confidence intervals, A/B testing, and experimental design.Programming: 1+ years of experience in Python and/or R, with proficiency in data science libraries such as Scikit-learn, Pandas, NumPy, SciPy, Statsmodels, and TensorFlow or PyTorch.Databases: Familiarity with SQL for data extraction and manipulation.Experience with Databricks (PySpark, Spark SQL, MLflow) is a significant plus.Familiarity with visualization tools such as Power BI, Matplotlib, Seaborn, or Python Dash is a plus.Strong written and verbal communication skills, with the ability to articulate complex analytical concepts in practical terms to non-technical audiences.Preferred QualificationsPh.D. candidate with research focus in time series forecasting, statistical learning, or applied machine learning.Experience working with large-scale datasets in a Databricks or Spark-based environment.Familiarity with demand forecasting, supply chain analytics, or CPG industry data.Experience with optimization methods (linear programming, mixed-integer programming) using tools such as Gurobi, PuLP, or SciPy.Exposure to MLOps practices, including model versioning, experiment tracking (e.g., MLflow), and pipeline automationIndividual base pay depends on work location and additional factors such as experience, job-related skills, and relevant education or training. Total pay may include other forms of compensation. In addition, we offer competitive health, dental, 401k and wellness benefits beginning on the first day of employment. Please ask your Talent Acquisition Partner for more information about our total rewards package.The Company is committed to providing equal opportunity for employees and qualified applicants in all aspects of the employment relationship, including consideration for employment, without regard to race, color, sex, sexual orientation, gender identity, national origin, citizenship, marital status, protected veteran status, disability, age, religion, or any other classification protected by law.