Position: Data Analyst
Company: inVerita
Experience required: 5y
Category: Data Analyst
English level: upper
Published: 2023-07-01T00:00:00+03:00

We are searching for a talented Data Analyst who will apply statistical analysis to find outliers, determine quality KPIs on different samples of data, and investigate how we can improve the overall quality of it. Then communicate and coordinate with the rest of the data team to implement the improvements.

**Project:** 
• Platform provides business data enrichment with unprecedented coverage of private companies, accurate classification, and in-depth insights based on real-time updates, for procurement, insurance, market intelligence, and more.

**Requirements:**
• 5+ years of experience as a Data Analyst;
• Knowledge & background in working with Excel;
• Attention to detail and the ability to maintain focus while analyzing (very!) large datasets;
• High speed and uncompromising quality in your work
• A growth mindset, able to capitalize on unprecedented contexts through your skills and abilities;
• An appetite to grapple with a variety of technical challenges
• Consistent performance over time coordinated with a matching set of values;
• Strong problem-solving skills that enhance the way you deal with the tension between brief and shipping;
• Basic understanding of SQL will be a plus;
• English level: Upper Intermediate.

**Responsibilities: **
• Perform data Quality Assurance activities;
• Apply basic statistical analysis (Excel or SQL) to find outliers and determine data quality KPIs on different samples of data;
• Research data sources from across the web;
• Prepare and check data samples to be shared with clients and partners. Work closely with the sales and business development team;
• Help the Data Science team find edge cases and measure the outcome of different implementation methods, in terms of impact on overall data quality;
• Work closely with AI engineers and help curate data samples and training datasets;
• Prepare test samples and measure results as part of the data science development cycle;
• Prototype quickly to solve thorny use cases, without getting stuck in theory, as we're prone to shipping early and often;
• Identify areas of opportunity and improvement.