1160 | Middle Data Analyst

Intetics Inc., a leading American technology company specializing in custom software application development, distributed professional teams creation, software product quality assessment, and “all-things-digital” solutions, is on the lookout for a  Middle Data Analyst  to join our team and provide exceptional customer support.

Position Overview

We are looking for a skilled Data Analyst to transform complex datasets into clear, actionable insights that support data-driven decision-making across the organization.

This position combines statistical and quantitative analysis, data visualization, research, and stakeholder communication. The ideal candidate can identify meaningful trends, validate analytical results, and clearly explain findings to both technical and non-technical audiences.

This is a full-time, long-term position with an immediate start.

Responsibilities

Data Analysis
- Analyze large and complex datasets using statistical and quantitative methods.
- Apply regression analysis, hypothesis testing, probability analysis, simulations, and time-series analysis to identify patterns and explain trends.
- Validate analytical outputs, identify anomalies and outliers, and distinguish meaningful issues from normal variability.
- Translate analytical findings into practical recommendations for customers and internal stakeholders.
- Support business decisions with clear, evidence-based insights.

Data Visualization and Reporting
- Design and develop dashboards, reports, and visualizations that make complex information easy to understand.
- Adapt reporting formats and levels of detail for different audiences, from executive summaries to detailed analytical reports.
- Monitor dashboards and reports to ensure their accuracy as data sources, business rules, and system configurations change.
- Present analytical findings clearly to technical and non-technical stakeholders.

Research and Data Quality
- Research relevant datasets, industry benchmarks, and external context to support customer questions and internal decision-making.
- Assess data quality and investigate inconsistencies or unexpected results.
- Use AI tools to accelerate research, summarize findings, prepare documentation, and support analytical and quality-assurance workflows.
- Stay informed about relevant analytical tools, data sources, methodologies, and industry best practices.