Data Analytics – Python / Pandas / NumPy

PT ELISTEC INFORMATIKA UTAMA; Jakarta Pusat; On-site; IDR 60,000,000 - 90,000,000; Full time

Job summary

PT ELISTEC INFORMATIKA UTAMA is seeking a data analyst with intermediate Python experience to analyze and process data. You will manipulate data with Pandas, perform cleaning and transformation, and execute aggregation and correlation analyses.

Familiarity with NumPy and basic visualization with Matplotlib/Seaborn is beneficial. The role focuses on preparing datasets from multiple sources (CSV/Excel/SQL), performing time-series analyses, and documenting analytical findings with clear results.

Qualifications

• Intermediate experience in Python for data analysis and processing.
• Proficient in using Pandas for data manipulation, cleansing, transformation, and aggregation.
• Familiar with NumPy for numerical analysis and data processing.
• Able to perform data cleansing, including handling missing values, duplicates, inconsistent formats, and invalid data.
• Able to perform data transformation and normalization to prepare datasets for further analysis.
• Experienced in performing data aggregation and grouping using Pandas.
• Able to perform correlation analysis to identify relationships between different variables or datasets.
• Familiar with Exploratory Data Analysis (EDA) to identify trends, patterns, anomalies, and data distributions.
• Able to work with large and multi-source datasets, preferably including network or telecommunications data.
• Familiar with handling time-series data using Python/Pandas.
• Able to create basic scripts for simple data processing and automation, such as recurring data cleansing, transformation, aggregation, or reporting tasks.
• Familiar with integrating data from CSV, Excel, SQL databases, or other structured data sources.
• Able to document data-processing steps and analytical findings clearly.
• Strong attention to detail and problem-solving skills.
• Experience with Matplotlib, Seaborn, or other visualization libraries is an advantage.

Responsibilities

• Work with Python/Pandas/NumPy to cleanse, transform, and aggregate data.
• Perform correlation analyses to uncover relationships between datasets.
• Conduct Exploratory Data Analysis to identify trends and anomalies.
• Handle large, multi-source datasets, possibly including network/telecom data.
• Develop simple Python scripts for recurring data processing and automation.

Skills

Python

Pandas

NumPy

Data cleansing

Data transformation

Data aggregation

Correlation analysis

EDA

Time-series data

Automation

Tools

Matplotlib

Seaborn

Job description

Have intermediate experience in Python for data analysis and processing.

Proficient in using Pandas for data manipulation, cleansing, transformation, and aggregation.

Familiar with NumPy for numerical analysis and data processing.

Able to perform data cleansing, including handling missing values, duplicates, inconsistent formats, and invalid data.

Able to perform data transformation and normalization to prepare datasets for further analysis.

Experienced in performing data aggregation and grouping using Pandas.

Able to perform correlation analysis to identify relationships between different variables or datasets.

Familiar with Exploratory Data Analysis (EDA) to identify trends, patterns, anomalies, and data distributions.

Able to work with large and multi-source datasets, preferably including network or telecommunications data.

Familiar with handling time-series data using Python/Pandas.

Able to create basic scripts for simple data processing and automation, such as recurring data cleansing, transformation, aggregation, or reporting tasks.

Familiar with integrating data from CSV, Excel, SQL databases, or other structured data sources.

Able to document data-processing steps and analytical findings clearly.

Strong attention to detail and problem-solving skills.

Experience with Matplotlib, Seaborn, or other visualization libraries is an advantage.

Key Competency:

Python / Pandas / NumPy — Intermediate proficiency in using Python for data cleansing, transformation, aggregation, correlation analysis, exploratory data analysis, and simple automation, with the ability to process and derive insights from structured and multi-source datasets.