Junior Data Scientist

About the role

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