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Data Engineer/Data Science

Avacend, Inc.Plano, TX🇺🇸United StatesPosted 20 Jul 2026

Why This Role Stands Out

This role offers an exciting opportunity to leverage your data modeling and machine learning expertise to analyze and predict network performance issues, contributing directly to critical infrastructure improvements. You will thrive here if you have a strong background in Python, Spark, and cloud development, eager to grow your skills within a collaborative Data Science & Tools Team. Apply now to make a significant impact in this dynamic, fully onsite position!

Quick Overview

Work Type
On Site
Level
Mid Senior

Job Description

Data Engineer/ Data Science on w2(No C2C)

Duration:  6+ Months

Work Location: Plano, TX.- Schedule: Fully onsite

Top skills:
-Data Modeling
-Machine Learning


KEY RESPONSIBILITES/REQUIREMENTS:
As a Performance Assurance Machine Learning Engineer, you will work under the coaching of Senior and Lead engineers of the Data Science & Tools Team You will utilize skills to query databases to extract data, use skills in Python or R to analyze data such that you can identify 4G/5G network infrastructure and performance issues and build prediction models, ad-hoc tools, and dashboards to communicate your findings with your team and peers

Background & Competencies Required:
• Graduate Degree in Computer Science, Statistics, Data Science or a related Data Engineering with 7+ years of professional experience is preferred.
• Programming experience: Python & Spark (preferred) and/or other languages such as R , SQL, Hive, Spark, Javascript, Visual Basic, C++, shell scripting in a linux or IDE environment such as VSCODE, Jupyter, RStudio, etc.
• Cloud Development Experience – AWS/Azure/Google utilizing cloud providers such as Databricks or Snowflake
• Machine learning expertise: GLM Regression (Linear, Logistic, Multinomial), Decision Tree (including Boosted Trees, Random Forest), kMeans/Hierarchical Clustering, Principle Component Analysis, t-SNE, Neural Networks such as transformers and auto-encoders, Bayesian Regression, and Times Series Modeling.
• Experience using data with high-volume (1TB+) & high-dimensionality (500+ variables per schema), especially within a big data framework (HaDoop, Citus, MongoDB, etc).
• Experience performing Data Wrangling, Exploratory Data Analysis (EDA), Correlation Analysis, Statistical Methodologies (distributions, hypothesis testing, confidence intervals) & Significance Testing, A/B Testing.

 

Skills

MongoDB
SQL
Shell
5G
AWS
Linear
Machine Learning
Snowflake
Azure
Databricks
C++
Hadoop
Hive
JavaScript
Jupyter
Python
R

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