Data Analyst

TEKsystems•Westerville, OH

5h•$45 - $50•Remote

About The Position

We are seeking a detail-oriented Data Analyst to evaluate, label, and analyze complex datasets that power our AI and machine learning models. In this role, you will go beyond basic data labeling by reviewing raw data to verify and correct existing labels, applying consistent annotations, and using Python to explore datasets, identify patterns in text and other data types, and interpret what the data represents. You will collaborate closely with our AI/ML engineering team to develop and refine annotation guidelines that improve data quality and model performance.

Requirements

3+ years' experience with Python - hands on experience writing scripts for data cleaning and preprocessing. Some existing scripts and tools are available but must be able to work independently and build, modify and optimize scripts.
Jupyter notebook
Should be comfortable pulling data from different sources - calling APIs and writing SQL queries.
Proven experience working with data annotation or labeling combined with strong data analysis skills
Ability to interpret complex datasets and understand underlying data patterns and nuances
Comfortable working with multiple data types and familiar with data management concepts
Basic proficiency with annotation platforms

Nice To Haves

Background in data science, statistics, machine learning, or related technical fields
Familiarity with data cleaning techniques and quality assurance practices
Familiarity with incident management, change management, or operational excellence frameworks
Experience using annotation platforms
Experience contributing to open source projects related to data annotation, machine learning, or AI infrastructure
Hands-on experience building or training machine learning models

Responsibilities

Analyze diverse datasets to understand context, quality, and labeling requirements
Perform accurate data annotation and labeling with strong attention to detail
Clean and preprocess datasets by identifying and correcting errors, inconsistencies, and missing data
Collaborate closely with engineers to develop and refine annotation guidelines and standards based on data insights
Identify inconsistencies, ambiguities, or anomalies in the data and provide actionable feedback
Support data quality assurance by reviewing and validating annotations for consistency and accuracy
Document data characteristics, annotation processes, and best practices to maintain knowledge continuity