Data Scientist Intern

Roles and responsibilities

• Conduct analyses on the effectiveness of product offerings by examining the real-world impacts of customer use.  For instance, do banks that assess cyber risk before partnering with a merchant suffer less downstream fraud?  How much money can a merchant save, in data breaches avoided, by subscribing to Mastercard’s cyber monitoring product?
• Gain subject matter knowledge on cybersecurity best practices and common types of fraud.  Advise technical teams in accordance with subject matter knowledge and participate in educational initiatives. Competently handle large datasets, sifting for patterns and trends and translate those insights into technical solutions and education materials.
• Work closely with marketing and product to translate analysis into promotional materials.
• Partner with other teams (Safety Net, Smart Authentication, Data Strategy) to combine multiple sources of data in new and insightful ways.
  
  

What you’ll need

• To be available to work Full-time between 26th May/2nd June 2025 to 9th August or late June to late August 2025
• To be a Penultimate Masters/Undergraduate Student, due to graduate no earlier than 2026
• Ideally enrolled in a bachelor's/Master's degree program in Computer science or related field
• Ideally focused in a field of data such as mathematics, statistics, or economics
• Insightful problem-solver, able to gather input from multiple divergent sources, narrow in on any problems or gaps, and propose effective solutions.
• Conversant with data: comfortable working with data sets of varying sizes, able to generate descriptive statistics and find simple patterns.  Bonus: knowledge of and experience with machine learning models.

Desired candidate profile

. Programming Skills

• Python: This is the most common language used in data science, particularly for data manipulation, analysis, and machine learning. Key libraries include:
  • Pandas: For data manipulation.
  • NumPy: For numerical operations.
  • Matplotlib/Seaborn: For data visualization.
  • Scikit-learn: For implementing machine learning algorithms.
• R: A popular language for statistical analysis and visualization.
• SQL: Proficiency in querying databases to extract, manipulate, and analyze data.

2. Data Analysis and Preprocessing

• Cleaning and Wrangling Data: Often, real-world data is messy and requires significant cleaning, which is an important aspect of the job.
• Exploratory Data Analysis (EDA): This involves understanding and visualizing data to discover patterns, relationships, or anomalies that can guide further analysis.
• Feature Engineering: Creating new features from raw data to improve model performance.

3. Machine Learning

• Supervised Learning: Familiarity with algorithms such as linear regression, decision trees, random forests, and support vector machines (SVM).
• Unsupervised Learning: Knowledge of clustering techniques like k-means, DBSCAN, and principal component analysis (PCA).
• Model Evaluation: Understanding how to assess models using metrics like accuracy, precision, recall, F1 score, and AUC-ROC curves.