Data Scientist Intern

3 weeks ago


Dubai, Dubai, United Arab Emirates Mastercard Full time
Roles and responsibilities
  • Conduct analyses on theeffectiveness of product offerings by examining the real-worldimpacts of customer use.  For instance, do banks that assess cyberrisk before partnering with a merchant suffer less downstreamfraud?  How much money can a merchant save, in data breachesavoided, by subscribing to Mastercard's cyber monitoringproduct?
  • Gain subject matter knowledge oncybersecurity best practices and common types of fraud.  Advisetechnical teams in accordance with subject matter knowledge andparticipate in educational initiatives. Competently handle largedatasets, sifting for patterns and trends and translate thoseinsights into technical solutions and educationmaterials.
  • Work closely with marketing andproduct to translate analysis into promotionalmaterials.
  • Partner with other teams (SafetyNet, Smart Authentication, Data Strategy) to combine multiplesources of data in new and insightful ways.

What you'llneed

  • To be available towork Full-time between 26th May/2nd June 2025 to 9th August or lateJune to late August 2025
  • To be a PenultimateMasters/Undergraduate Student, due to graduate no earlier than2026
  • Ideally enrolled in abachelor's/Master's degree program in Computerscience or related field
  • Ideally focused in afield of data such as mathematics, statistics, oreconomics
  • Insightful problem-solver, able togather input from multiple divergent sources, narrow in on anyproblems or gaps, and propose effectivesolutions.
  • Conversant with data: comfortableworking with data sets of varying sizes, able to generatedescriptive statistics and find simple patterns.  Bonus: knowledgeof and experience with machine learningmodels.
Desired candidate profile

.ProgrammingSkills

  • Python:This is the most common language used in data science, particularlyfor data manipulation, analysis, and machine learning. Keylibrariesinclude:
    • Pandas:For datamanipulation.
    • NumPy:For numericaloperations.
    • Matplotlib/Seaborn:For datavisualization.
    • Scikit-learn:For implementing machine learningalgorithms.
  • R:A popular language for statistical analysis andvisualization.
  • SQL:Proficiency in querying databases to extract, manipulate, andanalyze data.

2. DataAnalysis andPreprocessing

  • Cleaningand Wrangling Data: Often, real-world data is messyand requires significant cleaning, which is an important aspect ofthe job.
  • Exploratory DataAnalysis (EDA): This involves understanding andvisualizing data to discover patterns, relationships, or anomaliesthat can guide furtheranalysis.
  • FeatureEngineering: Creating new features from raw data toimprove model performance.

3.MachineLearning

  • SupervisedLearning: Familiarity with algorithms such as linearregression, decision trees, random forests, and support vectormachines (SVM).
  • UnsupervisedLearning: Knowledge of clustering techniques likek-means, DBSCAN, and principal component analysis(PCA).
  • ModelEvaluation: Understanding how to assess models usingmetrics like accuracy, precision, recall, F1 score, and AUC-ROCcurves.
Key Skills
Data Extraction,Data Science,Data Analysisand Reporting
Employment Type :Full-time
Department / Functional Area: Data Science
Experience: years
Gender: Male
Vacancy: 1
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