> Markdown version of [/jobs/ext/2210821-mid-level-data-scientist](https://www.wearedevelopers.com/jobs/ext/2210821-mid-level-data-scientist). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Mid Level Data Scientist - **Company:** Apex Systems LLC - **Location:** Minneapolis, MN, United States (Remote available) - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Optical Character Recognition (OCR), Big Data, Data Cleansing, Information Engineering, Python (Programming Language), Unstructured Data, Large Language Models, Snowflake, AWS Lambda, Production Code, Virtual Agents, Service Stack, Databricks - **Published:** August 24, 2026 - **Apply:** https://arc.dev/remote-jobs/j/redirect/peldpgnipl ## About the Role * Experience with Python. * Demonstrated experience with Agentic AI. * Familiarity with Large Language Models (LLMs), specifically Bedrock foundation models. * Experience in data science modeling. * Proficiency with Databricks or Snowflake. * Experience working with large datasets (hundreds of gigabytes to terabytes, billions of rows). * Knowledge of AWS, including Lambdas for orchestration. Preferred Qualifications * Familiarity with Document AI or Optical Character Recognition (OCR). * Experience in data engineering tasks such as unstructured data processing, data cleaning, standardization, and joining/enriching datasets. ## Description * Develop an automated process using AI-native tools to process and load unstructured data from sources like PDFs and emails into Databricks tables. * Build data science models to analyze user behavior at an aggregate level. * Create models that compare on-site behavior to audience quality data to determine campaign effectiveness. * Develop AI agents to extract key fields from unstructured sources, such as audience quality metrics and campaign performance data. * Standardize, clean, and enrich data by normalizing formats, resolving inconsistencies, and joining datasets. * Write production-grade code and work towards a deployable model. * Collaborate with engineering teams on integration with the in-house technology stack. ## Related Videos - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Server Side Serverless in Swift](https://www.wearedevelopers.com/videos/133-server-side-serverless-in-swift) - [How Cisco embraced a DevOps culture within its network engineering team](https://www.wearedevelopers.com/videos/99-how-cisco-embraced-a-devops-culture-within-its-network-engineering-team) - [Hacking AI at the Edge of the Indian Ocean](https://www.wearedevelopers.com/videos/100177-hacking-ai-at-the-edge-of-the-indian-ocean) - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) ## Related Articles - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know) - [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models)