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