Job Description: 

In this role, you will use statistical methods, algorithms, and machine learning techniques to extract insights from large datasets, often creating predictive models and identifying trends. As a Data Intern, you’ll work in the fintech space to solve problems and drive innovation by transforming raw data into actionable intelligence. 

 

Responsibilities:

  • Collect, process, and analyze large datasets to identify trends, patterns, and insights that can drive business decisions.
  • Design, develop, and implement statistical models and machine learning algorithms to solve complex business problems.
  • Clean and preprocess raw data to ensure accuracy, quality, and reliability for analysis.
  • Work closely with cross-functional teams, including product managers, engineers, and business analysts, to understand business objectives and deliver data-driven solutions.
  • Create clear and compelling visualizations to communicate data findings and insights to stakeholders, both technical and non-technical.
  • Design and conduct experiments, such as A/B testing, to validate hypotheses and measure the impact of changes or new initiatives.
  • Develop and maintain data pipelines and automated processes for continuous data collection, analysis, and reporting.
  • Prepare and present detailed reports and technical documentation to share methodologies, results, and recommendations with stakeholders.
  • Stay updated on the latest developments in data science, machine learning, and analytics, and apply new techniques and tools as appropriate.
  • Work with cybersecurity team to ensure compliance with all data handling and analysis practices including data privacy and security regulations.

 

Skills and Qualifications:

  • Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field.
  • Excellent verbal and written communication skills.
  • Strong analytical and problem-solving skills.
  • Strong programming skills in languages, preferably Python.
  • Deep understanding of statistical analysis, hypothesis testing, and experimental design.
  • Ability to create insightful and visually appealing data visualizations using tools such as Tableau and Power BI.
  • Familiarity with data engineering tools, cloud platforms, preferably Azure, and version control systems like Git.
  • Commitment to staying current with emerging trends and technologies in data science and machine learning.