Junior Data Analyst (Data Scientist)

Company Description
This opportunity is advertised on behalf of a partner company. All applications, interviews, and subsequent hiring steps will be managed directly by the partner organization.
Our partner is seeking a Junior Data Analyst / Data Scientist to join their remote team and contribute to data-driven projects across Financial Services, Retail, E-commerce, Logistics, Business Intelligence, Artificial Intelligence (AI), and Machine Learning (ML).
This role is well suited to an early-career data professional who is curious about how data can be used to solve business problems, uncover trends, and support better decision-making.
The successful candidate will have the opportunity to work on a variety of analytics and data science projects, ranging from business reporting and data visualization to predictive modeling and machine learning.
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 (EDA) to uncover trends, relationships, anomalies, and opportunities
Build and maintain reports, dashboards, KPIs, and Business Intelligence (BI) solutions
Perform descriptive and statistical analyses to support business and strategic decisions
Assist in developing and evaluating Machine Learning (ML) and predictive models
Contribute to customer, product, commercial, marketing, and operational analytics projects
Analyze areas such as 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 clear and meaningful data visualizations to communicate analytical findings
Present insights and recommendations to both technical and non-technical stakeholders
Contribute to AI, automation, and other data-driven initiatives
Help identify opportunities to improve data quality, reporting processes, and analytical workflows
Work collaboratively with teams across Product, Engineering, Finance, Marketing, Operations, and Business
Translate business requirements and questions into structured analytical approaches
Requirements
Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, Economics, Engineering, Business Analytics, Information Systems, or another quantitative discipline
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 (EDA)
Familiarity with Python data and Machine Learning libraries such as pandas, NumPy, and scikit-learn
Experience with or understanding of dashboarding and reporting tools such as Power BI, Tableau, Looker, or similar platforms
Strong analytical thinking and problem-solving abilities
Ability to interpret data and communicate insights in a clear and structured manner
Strong written and spoken English
Comfortable working both independently and as part of a distributed, remote team
Preferred Qualifications
Internship, academic, bootcamp, freelance, or personal project experience in Data Analytics, Data Science, Business Intelligence, or Machine Learning
Exposure to data-driven industries such as Financial Services, FinTech, Retail, E-commerce, Logistics, or Technology
Familiarity with Git and GitHub
Experience working with Jupyter Notebook
Basic exposure to cloud environments such as AWS, Microsoft Azure, or Google Cloud Platform (GCP)
Familiarity with modern data warehouses and platforms including BigQuery, Snowflake, Redshift, or Databricks
Experience using Excel or BI and visualization tools such as Power BI, Tableau, or Looker
Exposure to predictive modeling, Machine Learning, or statistical modeling techniques
Familiarity with Generative AI, Large Language Models (LLMs), or AI-based applications
Portfolio demonstrating practical data work, such as GitHub repositories, Kaggle notebooks, university assignments, bootcamp projects, or personal Data Analytics / Data Science projects

Work arrangement
No