Junior Data Analyst (Data Scientist)

Opportunity Overview

Key Responsibilities

• Collect, clean, transform, and analyze data from different internal and external sources.
• Use Python, SQL, and statistical techniques to investigate business and operational questions.
• Conduct Exploratory Data Analysis to uncover trends, relationships, anomalies, and opportunities.
• Build and maintain reports, dashboards, KPIs, and Business Intelligence solutions.
• Perform descriptive and statistical analyses to support business and strategic decisions.
• Assist in developing and evaluating Machine Learning and predictive models.
• Contribute to customer, product, commercial, marketing, and operational analytics projects.
• Analyze customer behavior, transactions, sales performance, product usage, and operational efficiency.
• Support forecasting, customer segmentation, classification, and other predictive analytics initiatives.
• Assist with A/B testing, experimentation, and hypothesis-driven analysis.
• Develop visualizations and present insights and recommendations to technical and non-technical stakeholders.
• Improve data quality, reporting processes, and analytical workflows while collaborating across teams.

Requirements

• Bachelor's degree in a quantitative discipline such as Data Science, Computer Science, Statistics, Mathematics, Economics, Engineering, Business Analytics, or Information Systems.
• Solid foundational knowledge of Python and SQL.
• Good understanding of statistics, probability, and fundamental data analysis concepts.
• Hands-on experience working with datasets and conducting Exploratory Data Analysis.
• Familiarity with pandas, NumPy, and scikit-learn.
• Experience with or understanding of Power BI, Tableau, Looker, or similar dashboarding and reporting platforms.
• Strong analytical thinking, problem-solving, data interpretation, and communication abilities.
• Strong written and spoken English.
• Comfort working independently and as part of a distributed, remote team.

Preferred Qualifications

• Internship, academic, bootcamp, freelance, or personal project experience in a relevant data field.
• Exposure to Financial Services, FinTech, Retail, E-commerce, Logistics, or Technology.
• Familiarity with Git and GitHub.
• Experience working with Jupyter Notebook.
• Basic exposure to AWS, Microsoft Azure, or Google Cloud Platform.
• Familiarity with BigQuery, Snowflake, Redshift, or Databricks.
• Experience using Excel or BI and visualization tools.
• Exposure to predictive modeling, Machine Learning, or statistical modeling techniques.
• Familiarity with Generative AI, Large Language Models, or AI-based applications.
• A portfolio demonstrating practical data work through repositories, notebooks, assignments, or projects.