1160 | 中级数据分析师
1160 | Middle Data Analyst
Intetics Inc. 是一家领先的美国科技公司，专注于定制软件应用开发、分布式专业团队建设、软件产品质量评估以及“全数字化”解决方案，正在寻找一名中级数据分析师加入我们的团队，为客户提供卓越的支持
职位概述
我们正在寻找一名技能娴熟的数据分析师，将复杂的数据集转化为清晰、可操作的见解，以支持组织内的数据驱动决策。
该职位结合统计和定量分析、数据可视化、研究以及与利益相关者的沟通。理想的候选人能够识别有意义的趋势，验证分析结果，并向技术和非技术受众清晰地解释发现。
这是一份全职、长期的工作，可以立即开始。
职责
数据分析
- 使用统计和定量方法分析大型和复杂的数据集。
- 应用回归分析、假设检验、概率分析、模拟和时间序列分析来识别模式并解释趋势。
- 验证分析结果，识别异常值和离群值，并区分有意义的问题与正常的变化。
- 将分析结果转化为对客户和内部利益相关者的实际建议。
- 通过清晰、基于证据的见解支持业务决策。
数据可视化和报告
- 设计和开发仪表盘、报告和可视化图表，使复杂的信息易于理解。
- 根据不同受众（从高管摘要到详细分析报告）调整报告格式和细节层次。
- 监控仪表盘和报告，确保其准确性，随着数据源、业务规则和系统配置的变化。
- 向技术和非技术利益相关者清晰地展示分析结果。
研究和数据质量
- 研究相关数据集、行业基准和外部背景，以支持客户问题和内部决策。
- 评估数据质量并调查不一致或意外的结果。
- 使用AI工具加速研究，总结发现，准备文档，并支持分析和质量保证流程。
- 关注相关的分析工具、数据源、方法论和行业最佳实践。
要求
- 统计学、数学、经济学、计算机科学或相关领域的学士学位，具有量化背景
查看英文原文
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
Originally posted on Himalayas