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Salary
$13k – $30k per year (Estimated)
Location
Remote (Kazakhstan)
Seniority
Middle
Overview
Company
Impact
Profile match
Intetics is an American software engineering company founded in 1995 that builds and operates distributed development teams for clients in Europe and North America. It works on custom software, data engineering, geospatial systems, quality assurance and technology audit, and it is known for a formalised model it calls Remote In-Sourcing, where the client effectively gains a dedicated engineering unit without establishing a foreign entity. Headquartered in Naples, Florida with delivery centres across eastern Europe, it also runs an independent software quality assessment practice used in due diligence.

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.

Requirements

  • Bachelor’s degree in Statistics, Mathematics, Economics, Computer Science, or another quantitative field.
  • At least 3 years of professional experience in data analysis, business intelligence, or a related role.
  • Strong knowledge of statistics and quantitative analysis.
  • Advanced SQL skills and experience working with large datasets.
  • Strong proficiency with at least one data visualization platform, such as Power BI or Tableau.
  • Proficiency in Python, R, or another statistical programming language.
  • Experience creating dashboards, reports, and analytical presentations for different stakeholder groups.
  • Excellent written and verbal communication skills, with the ability to explain complex findings in clear, practical language.

Preferred Qualifications

  • Master’s degree in a quantitative field.
  • Experience with cloud data platforms such as AWS, Microsoft Azure, or Google Cloud Platform.
  • Understanding of machine learning concepts and their practical applications.
  • Experience with A/B testing and experimental design.
  • Familiarity with data governance and data quality practices.
  • Experience using AI tools to improve research, analysis, documentation, or quality-assurance workflows.

Technical Skills

  • Statistical analysis: regression, hypothesis testing, probability distributions, simulations, and time-series analysis
  • Programming and querying: Python, R, SQL
  • Python libraries: pandas, NumPy, scikit-learn
  • Data visualization: Power BI, Tableau, matplotlib, ggplot2, or similar tools
  • Data tools: advanced Excel, Jupyter Notebook, Git
  • Databases: SQL Server, PostgreSQL, MySQL, or comparable relational databases
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