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# Data Scientist

- **Company:** Virtual Networx
- **Location:** Houston, TX, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Artificial Intelligence, Amazon Web Services, Data Analysis, Microsoft Azure, Big Data, Cloud Computing, Data Cleansing, Data Visualization, Apache Hadoop, Apache Hive, Python (Programming Language), Machine Learning, Natural Language Processing, NumPy, Recommender Systems, Power BI, Tensorflow, Scala (Programming Language), SQL Databases, Tableau (Software), Data Processing, Google Cloud, Feature Engineering, Pytorch, Large Language Models, Apache Spark, Deep Learning, Model Validation, Generative AI, Git, Pandas, Matplotlib, Scikit Learn, Kubernetes, Data Analytics, Machine Learning Operations, Software Version Control, Docker, Unsupervised Learning, Databricks
- **Published:** July 30, 2026
- **Apply:** https://www.dice.com/job-detail/17f005a8-0ae6-4bd6-8365-6a85a9e6bc4f

## About the Role

Skills: Python,PyTorch ,SQL, Scala, Java, Data Analysis, Data Visualization, Pandas, NumPy, , Tableau, Power BI,EDA, * Python (Pandas, NumPy, Scikit-learn)

 * SQL for data extraction and analysis
 * Statistics and Probability
 * Machine Learning (Supervised & Unsupervised Learning)
 * Data Cleaning and Feature Engineering
 * Data Visualization (Tableau, Power BI, Matplotlib, Seaborn)
 * Exploratory Data Analysis (EDA)
 * Predictive Modeling
 * Model Evaluation and Validation
 * Deep Learning (TensorFlow or PyTorch) preferred
 * Version Control (Git)
 * Cloud platforms (AWS, Azure, or Google Cloud)

Preferred Skills

 * NLP (Natural Language Processing)
 * Large Language Models (LLMs)
 * Generative AI
 * Apache Spark or Databricks
 * MLOps (MLflow, Kubeflow)
 * Docker and Kubernetes
 * Big Data technologies (Hadoop, Hive)
 * A/B Testing and Experimentation
 * Time Series Forecasting
 * Recommendation Systems

## Description

* Collect, clean, and analyze large datasets.
 * Build predictive and machine learning models.
 * Perform exploratory data analysis (EDA) to identify trends and insights.
 * Develop statistical models to solve business problems.
 * Create dashboards and visualizations for stakeholders.
 * Validate, monitor, and improve model performance.
 * Collaborate with data engineers, analysts, and business teams.
 * Present findings and recommendations to technical and non-technical audiences.
 * Automate data processing and model deployment workflows.
 * Stay current with new AI, ML, and data science technologies.


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