Junior Data Analyst (Data Scientist)

Company DescriptionThis 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 ResponsibilitiesCollect, clean, transform, and analyze data from different internal and external sourcesUse Python, SQL, and statistical techniques to investigate business and operational questionsConduct Exploratory Data Analysis (EDA) to uncover trends, relationships, anomalies, and opportunitiesBuild and maintain reports, dashboards, KPIs, and Business Intelligence (BI) solutionsPerform descriptive and statistical analyses to support business and strategic decisionsAssist in developing and evaluating Machine Learning (ML) and predictive modelsContribute to customer, product, commercial, marketing, and operational analytics projectsAnalyze areas such as customer behavior, transactions, sales performance, product usage, and operational efficiencySupport forecasting, customer segmentation, classification, and other predictive analytics initiativesAssist with A/B testing, experimentation, and hypothesis-driven analysisDevelop clear and meaningful data visualizations to communicate analytical findingsPresent insights and recommendations to both technical and non-technical stakeholdersContribute to AI, automation, and other data-driven initiativesHelp identify opportunities to improve data quality, reporting processes, and analytical workflowsWork collaboratively with teams across Product, Engineering, Finance, Marketing, Operations, and BusinessTranslate business requirements and questions into structured analytical approachesRequirementsBachelor's degree in Data Science, Computer Science, Statistics, Mathematics, Economics, Engineering, Business Analytics, Information Systems, or another quantitative disciplineSolid foundational knowledge of Python and SQLGood understanding of statistics, probability, and fundamental data analysis conceptsHands-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-learnExperience with or understanding of dashboarding and reporting tools such as Power BI, Tableau, Looker, or similar platformsStrong analytical thinking and problem-solving abilitiesAbility to interpret data and communicate insights in a clear and structured mannerStrong written and spoken EnglishComfortable working both independently and as part of a distributed, remote teamPreferred QualificationsInternship, academic, bootcamp, freelance, or personal project experience in Data Analytics, Data Science, Business Intelligence, or Machine LearningExposure to data-driven industries such as Financial Services, FinTech, Retail, E-commerce, Logistics, or TechnologyFamiliarity with Git and GitHubExperience working with Jupyter NotebookBasic 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 DatabricksExperience using Excel or BI and visualization tools such as Power BI, Tableau, or LookerExposure to predictive modeling, Machine Learning, or statistical modeling techniquesFamiliarity with Generative AI, Large Language Models (LLMs), or AI-based applicationsPortfolio demonstrating practical data work, such as GitHub repositories, Kaggle notebooks, university assignments, bootcamp projects, or personal Data Analytics / Data Science projects