Trainee Data Scientist

About Company :

Equifax is a global information solutions company that uses trusted unique data, innovative analytics, technology and industry expertise to power organizations and individuals around the world by transforming knowledge into insights that help make more informed business and personal decisions. Headquartered in Atlanta, Ga., Equifax operates or has investments in 24 countries in North America, Central and South America, Europe and the Asia Pacific region.

It is a member of Standard& Poor’s (S&P) 500® index and its common stock is traded on the New York Stock Exchange (NYSE) under the symbol EFX. With a global legacy of over 123 years in the credit industry. The company organizes, assimilates and analyzes data on more than 820 million consumers and more than 91 million businesses worldwide, and its database includes employee data contributed from more than 10,110 employers.

Job Description :

Join our Data and Analytics team as a Data Scientist, where you’ll play a pivotal role in delivering analytical solutions in the areas of Retail Bank Marketing and Risk Analytics for clients in India and the Middle East and help consumers live their finical best.

We are looking to hire a highly creative data scientist to address data analytics challenges in our organization, to collect large volumes of data from varying sources, clean and interpret data, create solutions to overcome challenges and communicate with interested parties.

Role & Responsibility :

• Developing and implementing statistical models and machine learning algorithms to predict credit risk, customer behavior, and fraud patterns.
• Collaborating with cross-functional teams to understand business requirements and translate them into actionable data insights.
• Performing exploratory data analysis, feature engineering, and model evaluation.
• Ensuring data quality and integrity through rigorous data cleaning and validation processes.
• Staying up-to-date with the latest advancements in data science and applying them to business problems.

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Required Skills:

• Statistical and Machine Learning Techniques: Proficiency in statistical modeling, machine learning algorithms (regression, classification, clustering, etc.), and their implementation.
•  Data Manipulation and Analysis: Expertise in SQL, Python (or similar programming languages), and data manipulation tools (Pandas, NumPy) to extract, clean, and analyze large datasets.
•  Problem-solving and Analytical Skills: Ability to break down complex problems, identify patterns, and draw meaningful conclusions from data.
• Communication Skills: Effective communication of technical concepts to both technical and non-technical audiences.

What could set you apart

• While the above qualifications are essential, certain qualities can make you stand out:
• Domain Knowledge: Understanding of the credit industry, risk modeling, and fraud detection is a significant advantage.
•  Passion for Data: A genuine interest in exploring data, uncovering insights, and experimenting with new methodologies.
•  Business Acumen: Ability to connect data insights to business outcomes and propose data-driven solutions.
•  Innovation: A creative mindset to explore new approaches and technologies to solve complex problems.
•  Team Collaboration: Strong interpersonal skills and the ability to work effectively in a collaborative environment.