Junior Data Analyst / Data Scientist

Role Overview

Key Responsibilities

• Analyze structured and unstructured datasets using Python, SQL, and statistical methods.
• Perform Exploratory Data Analysis to identify trends, patterns, anomalies, and business opportunities.
• Develop recurring reports, dashboards, KPIs, and business intelligence solutions.
• Extract, clean, transform, and validate data from multiple sources.
• Conduct descriptive and statistical analysis to support business decisions.
• Develop basic predictive and machine learning models under senior guidance.
• Support customer, product, sales, marketing, and operational analytics initiatives.
• Analyze customer behavior, business performance, transactions, and operational metrics.
• Assist with forecasting, segmentation, classification, and predictive analytics projects.
• Design and analyze A/B tests, experiments, and hypothesis tests when required.
• Create visualizations, communicate insights, and translate business questions into recommendations.
• Collaborate with Product, Engineering, Finance, Marketing, Operations, and Business teams.

Requirements

• Bachelor's degree in a quantitative or related field such as Data Science, Computer Science, Statistics, Mathematics, Economics, Engineering, Business Analytics, or Information Systems.
• Strong foundational knowledge of Python and SQL.
• Understanding of statistics, probability, and data analysis concepts.
• Experience working with datasets and performing Exploratory Data Analysis.
• Experience creating dashboards or reports with Tableau, Power BI, Looker, Metabase, or similar tools.
• Strong analytical, problem-solving, communication, and collaboration skills.
• Strong written and verbal English communication skills.
• Ability to work independently and collaboratively in a remote environment.

Preferred Qualifications

• Internship, academic, bootcamp, or project experience in data analytics, data science, business intelligence, or machine learning.
• Exposure to financial services, fintech, retail, e-commerce, logistics, or other data-intensive industries.
• Familiarity with Git, GitHub, Jupyter Notebook, cloud platforms, or modern data platforms.
• Exposure to machine learning, predictive analytics, statistical modeling, generative AI, large language models, or AI-powered applications.
• A GitHub portfolio, Kaggle projects, university projects, bootcamp projects, or personal data projects is preferred.