Data Analyst (Entry Level)

Everscale Group; México; Presencial; MXN 275,000 - 459,000; Jornada completa

Descripción de la vacante

A dynamic analytics firm in Mexico is seeking an Entry Level Data Analyst to interpret and analyze large datasets. This role involves working closely with various departments, creating actionable insights, and presenting findings effectively. Ideal candidates should have a strong grasp of data analysis tools and techniques, along with excellent communication skills. This is an excellent opportunity for recent graduates to advance their careers in data analytics.

Formación

• Strong understanding of data collection, analysis, and visualization techniques.
• Ability to interpret complex datasets and generate actionable insights.
• Familiarity with statistical analysis and predictive modeling.

Responsabilidades

• Examine, interpret, and analyze large datasets to drive business decisions.
• Create visualizations to present findings to stakeholders.
• Collaborate with teams to develop data-driven strategies.

Conocimientos

Data Collection and Preparation

Data Analysis and Exploration

Data Visualization

Reporting and Presentation

Predictive Modeling

Data Quality Assurance

Collaborative Partnerships

Continuous Learning and Skill Development

Herramientas

Python

R

SQL

Tableau

Power BI

Descripción del empleo

We are looking for a Data Analyst Entry Level who is responsible for examining, interpreting, and analyzing large datasets to identify patterns, trends, and insights that can drive business decisions and strategies. They work closely with stakeholders from various departments to understand their data needs and deliver actionable recommendations based on their findings. Data Analyst Entry Level plays a vital role in helping organizations make data-driven decisions and optimize their operations.

Skills:

• Data Collection and Preparation: Data analysts gather relevant data from various sources, such as databases, spreadsheets, and APIs. They clean and preprocess the data to ensure its quality and reliability. This involves handling missing values, removing duplicates, and resolving
  inconsistencies.
• Data Analysis and Exploration: Using statistical techniques and analytical tools, data analysts explore and analyze the data to identify patterns, correlations, and trends. They apply descriptive and inferential statistical methods to gain insights into the data and answer specific business questions.
• Data Visualization: Data analysts create meaningful visualizations, such as charts, graphs, and dashboards, to present their findings in a clear and concise manner. Visualizations help stakeholders understand complex data and derive actionable insights at a glance.
• Reporting and Presentation: Data analysts prepare reports and presentations summarizing their analysis, insights, and recommendations. They communicate their findings effectively to both technical and non-technical audiences, highlighting the key takeaways and implications for decision-making.
• Predictive Modeling: In some cases, data analysts build predictive models using techniques like regression, classification, or time series analysis. These models help forecast future trends, predict customer behavior, or estimate business outcomes. They evaluate model performance and iterate as needed.
• Data Quality Assurance: Data analysts ensure the accuracy, consistency, and integrity of the data they work with. They perform data validation checks, assess data quality, and identify any anomalies or data issues. They also collaborate with data engineers to improve data
  infrastructure and data governance processes.
• Collaborative Partnerships: Data analysts collaborate with cross-functional teams, including business stakeholders, data scientists, and IT professionals. They understand business requirements and work together to define analytical goals, develop data-driven strategies, and
  implement solutions to address specific challenges.
• Continuous Learning and Skill Development: Data analysts stay updated with the latest industry trends, tools, and techniques in data analytics. They continuously enhance their skills in areas such as statistical analysis, programming languages (e.g., Python, R, SQL), data visualization tools (e.g., Tableau, Power BI), and data modeling concepts.