Data Scientist Intern

Plymouth Rock AssuranceWoodbridge Township, NJ
5d$35 - $35

About The Position

As a Data Scientist Intern, you will work on cutting-edge analytical and data engineering projects that drive measurable business impact across pricing, underwriting, marketing, and claims. This internship is ideal for a technically curious, motivated problem-solver who wants hands-on data science experience.

Requirements

  • Currently pursuing or recently completed a Master’s in Data Science, Computer Science, Statistics, Economics, or related field.
  • Proficiency in Python (Pandas, NumPy, Scikit-learn, XGBoost, or PyTorch) and SQL.
  • Understanding of data engineering concepts, ETL/ELT workflows, and machine learning deployment.
  • Exposure to workflow orchestration tools (e.g., Airflow, Dagster, Prefect) and Git/GitHub for collaborative development.
  • Strong analytical, communication, and problem-solving skills.
  • A self-starter mindset, with attention to detail and enthusiasm for learning new technologies.

Nice To Haves

  • Familiarity with Docker, CI/CD pipelines, and infrastructure-as-code tools such as Terraform preferred.
  • Knowledge of AWS cloud services such as S3, Lambda, EC2, or SageMaker a plus.
  • Experience with common modeling techniques (e.g., GLM, tree-based models, Bayesian statistics, NLP, deep learning) through coursework or projects.

Responsibilities

  • Support the design, construction, and optimization of robust data pipelines to enable machine learning and analytical modeling.
  • Contribute to the design and implementation of data and ML workflows using orchestration tools such as Dagster, Airflow, or similar frameworks.
  • Help implement data quality checks, validation routines, and monitoring for automated data workflows.
  • Assist in organizing and managing internal GitHub repositories to standardize ML project structures and best practices.
  • Collaborate with data scientists and engineers to automate the ingestion, transformation, and delivery of data for model development.
  • Contribute to initiatives migrating analytical processes into cloud-based data lake architectures and modern platforms such as AWS or Snowflake.
  • Develop reusable and well-tested code to support analytical pipelines and internal tools using Python and SQL.
  • Conduct data mining, cleansing, and preparation tasks to build high-quality analytical datasets.
  • Participate in model development, including data profiling, model training, validation, and interpretation.
  • Build and evaluate predictive models that enhance profitability through improved segmentation and estimation of insurance risk.
  • Assist in studies evaluating new business models for customer segmentation, retention, and lifetime value.
  • Collaborate with business leaders to translate insights into operational improvements and cost efficiencies.
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