Senior Data Analyst

North Inc
Raleigh, United States of America
6 days ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English
Experience level
Senior

Job location

Raleigh, United States of America

Tech stack

Microsoft Excel
Azure
Cloud Database
Computer Programming
Databases
Data Discovery
ETL
Data Mining
Database Design
R
SPSS (Software)
Python
Machine Learning
NumPy
Power BI
TensorFlow
SAS (Software)
SQL Databases
Tableau
Scripting (Bash/Python/Go/Ruby)
PyTorch
Deep Learning
Keras
Pandas
Matplotlib
Scikit Learn
Information Technology
Data Analytics
XGBoost
Data Management
Custom Reports
Databricks

Job description

Job Description : Mentor junior data analysts. Identify enterprise and departmental data elements and facilitate management of that data. Develop and implement data collection and management strategies that optimize quality and consistency. Proactively maintain the data for accuracy, completeness, consistency, validity, integrity and timelines. Work with management to prioritize business and information needs, visualizations, and data discovery. Locate and define new process improvement opportunities.

Requirements

Minimum Requirements : Position requires a Bachelor of Science degree (or foreign equivalent) in Data Science, Applied Data Analytics, Computer Science, or a related field of study. Position requires three (3) years of experience as a Data Analyst or a related occupation. Position requires three (3) years of experience with each of the following: machine learning algorithms, including supervised, unsupervised, and deep learning techniques (RandomForest, Decision Tree, Gradient Boosting, LGBM, and regression); libraries including scikit-learn, TensorFlow, PyTorch, Keras, Pandas, NumPy, and Matplotlib, Seaborn; information technology systems, data management, and reporting; reporting packages, databases (SQL), cloud data stores and development environments (Databricks or Azure) programming (python, scripting or ETL frameworks); statistics and using statistical packages for analyzing datasets (Excel, R, SPSS, or SAS Data Management); data models, visualization, database design, data mining and segmentation techniques; queries, report writing and presenting findings, dashboarding, and visualization (PowerBi, or Tableau).

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