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 Scientist to join their remote team and contribute to data-driven initiatives across Financial Services, FinTech, Artificial Intelligence (AI), Machine Learning (ML), Risk Analytics, Customer Analytics, and Business Intelligence.
- The role is suited to an early-career data professional interested in applying analytical and technical skills to real-world business and financial challenges.
- The successful candidate will work with diverse datasets, explore complex business problems, develop analytical and Machine Learning solutions, and help translate data into meaningful insights and recommendations.
- This position offers exposure to a range of data science initiatives, including predictive analytics, financial analytics, customer insights, experimentation, and AI-driven solutions.
Key Responsibilities
- Analyze structured and unstructured financial and business datasets using Python, SQL, and statistical techniques
- Conduct Exploratory Data Analysis (EDA) to uncover trends, relationships, anomalies, and potential business opportunities
- Develop, test, and evaluate Machine Learning (ML) models for use cases such as prediction, classification, segmentation, and forecasting
- Support analytics initiatives across financial risk, fraud, customer behavior, transactions, and business operations
- Prepare and transform datasets for analytical and Machine Learning applications
- Build data visualizations, dashboards, reports, and performance metrics to communicate findings effectively
- Apply statistical methods, hypothesis testing, and experimental approaches to business and product questions
- Contribute to predictive analytics and forecasting initiatives
- Investigate patterns across customer activity, financial performance, transactions, and operational data
- Support Artificial Intelligence (AI), automation, and data-driven product initiatives
- Clean, transform, validate, and document data to ensure reliability and usability
- Collaborate with Data Science, Data Engineering, Software Engineering, Product, Risk, Finance, and Business teams
- Present analytical findings and recommendations clearly to both technical and non-technical stakeholders
- Contribute to improving data quality, analytical processes, documentation, and reporting workflows
Requirements
- Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, Economics, Engineering, Business Analytics, Information Systems, or another quantitative discipline
- Strong foundational knowledge of Python and SQL
- Understanding of Machine Learning principles and commonly used modeling approaches
- Solid knowledge of statistics, probability, hypothesis testing, and experimental concepts
- Familiarity with Exploratory Data Analysis (EDA) and working with real-world datasets
- Familiarity with Python libraries such as pandas, NumPy, and scikit-learn
- Familiarity with data visualization and reporting tools such as Tableau, Power BI, Looker, or similar platforms
- Strong analytical reasoning and problem-solving skills
- Ability to work with complex datasets and identify meaningful patterns and insights
- Strong written and verbal English communication skills
- Ability to work effectively both independently and collaboratively in a remote environment
Preferred Qualifications
- Internship, academic, bootcamp, freelance, or personal project experience in Data Science, Machine Learning, Data Analytics, or related areas
- Exposure to financial data, transaction analytics, credit risk, fraud detection, customer analytics, or FinTech
- Familiarity with Git and GitHub
- Experience working with Jupyter Notebook
- Familiarity with cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform (GCP)
- Exposure to modern data platforms and warehouses such as BigQuery, Snowflake, Redshift, or Databricks
- Experience with BI and visualization tools including Tableau, Power BI, Looker, or similar platforms
- Exposure to Generative AI, Large Language Models (LLMs), or AI-powered applications