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 SQLGood 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, Num Py, 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, Fin Tech, Retail, E-commerce, Logistics, or Technology Familiarity with Git and Git Hub 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 Big Query, 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 Git Hub repositories, Kaggle notebooks, university assignments, bootcamp projects, or personal Data Analytics / Data Science projects