Data Analyst

Leidos is seeking a highly analytical and curious Data Analyst who is passionate about turning data into actionable business insights. The ideal candidate combines strong technical expertise with critical thinking and exceptional communication skills. This role requires someone who can work independently, uncover meaningful patterns in complex datasets, and communicate findings in a way that drives informed business decisions.\n \n, * Clearance: Secret clearance or ability to obtain one\n

• LocaBachelor’s degree in Data Analytics, Computer Science, Statistics, Mathematics, Engineering, Economics, or a related quantitative field (or equivalent practical experience).\n
• Strong proficiency in Python for data analysis and automation.\n
• Advanced SQL skills with experience querying and manipulating relational databases.\n
• Experience cleaning, transforming, and preparing data for analysis.\n
• Solid understanding of statistics and data analysis methodologies.\n
• Working knowledge of machine learning concepts and algorithms, including regression, classification, clustering, decision trees, supervised learning, and unsupervised learning.\n
• Familiarity with Large Language Models (LLMs), AI tools, and their practical application in data analysis.\n
• Ability to perform exploratory data analysis and communicate meaningful insights from complex datasets.\n
• Excellent analytical, critical thinking, and problem-solving skills.\n
• Ability to work independently, prioritize competing tasks, and investigate issues with minimal supervision.\n
• Strong verbal and written communication skills with the ability to explain technical concepts to non-technical audiences.\n, * Experience with data visualization tools such as Power BI, Tableau, or Looker.\n
• Experience using cloud-based data platforms (AWS, Azure, or Google Cloud).\n
• Familiarity with version control systems such as Git.\n
• Experience working with business intelligence, analytics, or AI-driven decision support environments.\n

• Extract, clean, transform, and validate data from multiple sources, including relational databases.\n
• Write efficient SQL queries to retrieve, manipulate, and analyze large datasets.\n
• Develop Python-based data analysis workflows for data exploration, modeling, and reporting.\n
• Perform exploratory data analysis (EDA), including univariate, bivariate, and multivariate analysis.\n
• Apply statistical methods and predictive modeling techniques such as linear regression and other statistical evaluations.\n
• Utilize AI and machine learning tools to identify trends, generate insights, and improve analytical processes.\n

• Build and evaluate machine learning models, including: \n \n

• Regression\n
• Classification\n
• Clustering\n
• Decision Trees\n
• Supervised Learning\n

• Unsupervised Learning\n \n

• Investigate data anomalies and independently determine the root causes behind unexpected results.\n
• Identify the most relevant data elements, metrics, and business questions without requiring extensive direction.\n
• Translate complex analytical findings into clear, compelling presentations and recommendations for non-technical stakeholders.\n
• Develop a deep understanding of the business and industry to ensure analyses align with organizational goals.\n
• Partner with business leaders to provide data-driven recommendations that support strategic decision-making.\n