Data Analyst Job at PayPal | (2 yrs) EXP Apply Right Now

A Data Analyst collects, strategizes, and plays statistical analyses on big datasets. They identify traits, create visualizations, and generate actionable insights to support business choices. Proficiency in information tools, SQL, and statistical software programs is important. Strong analytical capabilities and interest in elements are critical for success in this function.

Job Role: Data Analyst
Salary: ₹18.3 Lakhs Per Year
Location: Bengaluru, Karnataka
Company: PayPal
Qualification: Bachelor’s or Master’s degree
Experience: 2 years

About Company:

Data Analyst

PayPal is a leading online payment platform that facilitates transactions globally. Founded in 1998, it permits people and companies to make bills and cash transfers securely via the Internet. Users hyperlink their bank accounts or credit score playing cards to their PayPal debts, enabling them to send and receive payments seamlessly. PayPal operates in over 200 markets international and supports multiple currencies. It provides offerings like PayPal Checkout for merchants and PayPal.Me for personal transactions, improving comfort in online economic transactions. With a strong awareness of security and user protection, PayPal continues to innovate in digital payments generation.

Role Overview:

PayPal is searching for a Data Analyst in Bengaluru, Karnataka. This complete-time position entails enhancing credit danger information and reporting tools, riding analytical initiatives, and providing insights on client loan products. Requirements encompass a quantitative degree, 2+ years of experience, proficiency in SQL, Python, and Tableau, and sturdy analytical capabilities. Benefits include flexible painting surroundings, worker proportion alternatives, and health insurance. PayPal promotes diversity and gives reasonable hotels for disabilities. Data Analyst

Roles and Obligations Of PayPal Data Analyst:

Data Collection and Management:

Gathering records from numerous assets together with databases, APIs, or flat files. Ensuring records are satisfactory, cleaning, and organizing datasets for evaluation.

Data Analysis and Interpretation:

Applying statistical techniques and information visualization equipment to investigate data, and perceive trends, patterns, and correlations. Interpreting effects to extract actionable insights.

Report Generation and Presentation:

Creating clean and concise reports, dashboards, and shows to speak findings to stakeholders. Ensuring the facts are available and understandable for non-technical audiences.

Database Querying and Programming:

Using SQL or other programming languages (like Python or R) to govern records, carry out calculations, and automate procedures. Proficiency in querying databases is essential for extracting applicable datasets.

Problem-Solving and Decision Support:

Collaborating with groups to recognize enterprise needs and provide data-pushed answers. Supporting choice-making approaches by presenting analytical insights and recommendations based on statistics analysis.

Qualification:

A Bachelor’s or Master’s degree in a quantitative discipline such as Computer Science, Statistics, or Mathematics.
At least 2 years of professional experience in a related field.

Skills Required For Data Analyst:

Data Analysis:

Data Analyst

Proficiency in reading massive datasets with the use of statistical methods and gear like Excel, Python, or R. This consists of information cleaning, manipulation, and visualization to derive actionable insights.

SQL Database/Querying:

Ability to retrieve and control statistics from databases with the usage of SQL (Structured Query Language). Understanding database structures, joins, and aggregations is vital for querying and extracting relevant information.

Statistical Knowledge:

Familiarity with statistical techniques along with speculation testing, regression evaluation, and clustering. These abilties assist in know-how statistics distributions, correlations, and making facts-pushed decisions.

Programming Skills:

Knowledge of programming languages along with Python, R, or SAS for information evaluation and automation duties. This consists of writing scripts, and features, and making use of libraries for statistical computing and machine mastering.

Data Visualization:

Proficiency in creating visible representations of information (charts, graphs, dashboards) and the usage of tools like Tableau, Power BI, or matplotlib in Python. Effective visualization aids in conveying insights to stakeholders in reality and successfully.