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# Senior Data Scientist - ML, Python

- **Company:** Vsion Technologies Inc
- **Location:** Minneapolis, MN, United States
- **Experience:** Expert
- **Contract:** Contract
- **Skills:** Query Performance, Microsoft Excel, Application Programming Interfaces (APIs), Business Analytics Applications, Big Data, Cloud Storage, Continuous Integration, Data Architecture, Information Engineering, Data Integration, Data Integrity, Extract Transform Load (ETL), Relational Databases, Database Schema, File Transfer Protocol (FTP) SSL Extension, Apache Hive, JSON, Python (Programming Language), Machine Learning, Microsoft SQL Server, Performance Tuning, Cloud Services, Tensorflow, SONAR (Symantec), SQL Databases, Extensible Markup Language (XML), Parquet, Pytorch, Snowflake, Apache Spark, Git, Pyspark, Deployment Automation, Terraform, Data Pipelines, Databricks, Artifactory
- **Published:** November 14, 2026
- **Apply:** https://www.dice.com/job-detail/5d36d8ef-f033-452a-b229-e0e5d46ee37d

## About the Role

Mandatory skills - MS SQL,Machine Learning and Statistical modeling, PyTorch, Python, TensorFlow, 7 + years of experience in data engineering, data integration, data modeling, data architecture, and ETL/ELT processes to provide quality data and analytics solutions
5 + years of experience in Python
2 + years of experience in Apache Spark (PySpark/Spark SQL)
2+ years building and deploying Cloud based solutions using - Azure Databricks with UC, Snowflake, Functions, Service Bus
2+ years of experience in SQL with designing complex data schemas and query performance optimization
Experience with DevOps automation with Terraform.
Experience with CI/CD process and tools - GitHub Actions, GIT, Artifactory, Sonar

## Description

Design and develop ETL/ELT solutions on Azure Databricks, LakeBase and Spark
Develop, implement, and deploy large scale data pipelines empowering machine learning algorithms, insights generation, business intelligence dashboards, reporting and new data products
Design, build, optimize, and manage modern large-scale data pipelines ETL/ELT processing to support data integration for analytics, machine learning features and predictive modelling
Consume data from a variety of sources (RDBMS, APIs, FTPs and other cloud storage) & formats (Excel, CSV, XML, JSON, Parquet, Unstructured)
Write advanced / complex SQL with performance tuning and optimization
Identify ways to improve data reliability, data integrity, system efficiency and quality
Participate in architectural evolution of data engineering patterns, frameworks, systems, and platforms including defining best practices and standards for managing data collections and integration
Mentor other data engineers and provide technical direction by teaching other data engineers how to leverage cloud data platforms