Data Scientist Intern

  • Rockville, MD
  • Posted 11 hours ago | Updated 11 hours ago

Overview

On Site
Depends on Experience
Full Time
No Travel Required
Unable to Provide Sponsorship

Skills

Artificial Intelligence
Big Data
Data Engineering
Dashboard
Data Science
Data Visualization
Database
Databricks
Fraud
Health Care
Logistic Regression
Machine Learning (ML)
Monte Carlo Method
Pandas
Performance Tuning
Python
R
Regression Analysis
Software Engineering
SQL
Statistical Models
Statistics
TensorFlow
Testing
scikit-learn

Job Details

Data Science - AI/ML Intern

  • Must be local to Montgomery County, Maryland; preferably a entry level individual

  • On-site 3 days per week in Rockville, MD

  • Potential for full-time employment upon successful completion of the internship

Help team collect, clean, analyze, and visualize data to find insights, often using Python/R, SQL, and ML tools to solve business problems like churn or fraud, working on tasks from exploratory analysis and building dashboards to testing models, all while learning industry standards and software engineering basics. The specific duties vary greatly, from data engineering (pipelines) to core machine learning, depending on the company's needs. 

Required Skills

  • Experience with statistical modeling including logistic regression, Cox regression, and survival analysis
  • Experience with Monte Carlo simulation techniques
  • Experience building machine learning pipelines using tools such as scikit learn, TensorFlow, or PyTorch
  • Strong understanding of feature engineering for healthcare data including lab values, BMI, and comorbidities

Role and responsibilities:

  • Build and refine prediction models
  • Design and run Monte Carlo simulations for population level risk analysis
  • Validate model outputs and support performance tuning based on results

Key Skills & Tools Used

  • Programming: Python (Pandas, Scikit-learn, TensorFlow) or R.
  • Databases: SQL.
  • Concepts: Statistics, Machine Learning fundamentals, Software Engineering basics.
  • Tools: Data visualization software, potentially big data platforms (Databricks). 
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