Data Scientist

Lexical Intelligence, LLC is seeking a Data Scientist in Rockville, MD to serve as a technical advisor and support analytics needs. The role involves analyzing complex datasets, developing data solutions, and collaborating with business analysts to translate business needs into actionable insights.

Responsibilities:

• Serve as a technical advisor and support analytics needs
• Understand emerging technologies and demonstrate the ability to analyze and solve problems
• Partner with business analytics to frame and translate business needs into appropriate research questions for the team
• Collaborate with the team to provide strategic assessments of methods and technologies related to development and design standard operating procedures
• Use Python and alternate programming languages, such as Java and SQL, to mine complex dataset from different varieties of sources and platforms
• Manage data pipelines, develop data tables, create relevant scripts and code for analytical purposes
• Execute models and apply statistical tools to perform data analysis
• Develop well-managed data solutions to automate the frameworks of business logic/rules for data analysis
• Build Dashboards to demonstrate results and data findings for clients/customers
• Partner with the business analysts to provide consultancy and translate the business needs to design and develop tools, techniques, metrics, and dashboards for insights and data visualization
• Work directly with end users and managers to procure business requirements and translate into technology work instructions
• Drive analysis that provides meaningful insights on business strategies
• Drive an understanding and adherence to the principles of data quality management including metadata, lineage, and business definitions
• Work collaboratively with appropriate tech teams to build database and data access governance
• Collaborate, create and organize database in Azure SQL, provide a single Python API to update and optimize the efficiency of data query
• Draft documentation to monitor and report on data management and data quality
• Build pipelines for data exploration and visualize workforce data in Python
• Use Principal Component Analysis (PCA) for dimensionality reduction
• Use K-means clustering and Hierarchical clustering to discover conceptually meaningful classes of object
• Use NumPy, Pandas, Matplotlib, Seaborn, Plotly, Scikit-Learn libraries to visualize data trends and export template deliverables
• Build an interactive dashboard by Streamlit library for Demonstration