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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning & Data Science - **Company:** Lancesoft, Inc. - **Location:** Minneapolis, MN, United States - **Salary:** $172,640.0 - $189,072.0 - **Contract:** Temporary contract - **Skills:** Artificial Intelligence, Airflow, BigQuery, Cloud Computing, Cloud Storage, Cluster Analysis, Configuration Management, Software Quality, Databases, Continuous Delivery, Continuous Integration, Data Validation, Information Engineering, Data Files, Database Design, Distributed Computing Environment, Apache Hadoop, Hadoop Distributed File System, Apache Hive, Python (Programming Language), Key Management, Machine Learning, Network Connections, Performance Tuning, Software Engineering, Parquet, Data Processing, Feature Engineering, Apache Yarn, Prophet, Apache Spark, Model Validation, Caching, Git, Pandas, Pytest, Containerization, Pyspark, Kubernetes, Dask, Code Inspection, Data Management, Machine Learning Operations, Software Coding, Terraform, Code Restructuring, Software Version Control, Data Pipelines, Docker - **Published:** August 7, 2026 - **Apply:** https://www.careerbuilder.com/job-details/development-engineer-minneapolis-mn--e82508b7-a4f5-4ba3-ae53-30d7cb98edd7 ## About the Role * Experience building and deploying ML models in production environments * Hands-on experience with time series forecasting (Prophet, ARIMA, or similar) * Understanding of hyperparameter tuning, model validation, and experiment tracking * Familiarity with feature engineering and feature store concepts Data Engineering & Scalability * Proficiency converting pandas-based workloads to PySpark for large-scale processing * Experience with distributed data processing frameworks (Spark, Dask, or Ray) * Ability to optimize data pipelines for performance and cost efficiency * Working knowledge of data formats (Parquet, CSV) and partitioning strategies * Experience with BigQuery or similar analytical databases (table design, partitioning, clustering, writing/validating datasets) ML Pipeline Orchestration * Experience building ML pipelines using Kubeflow Pipelines (KFP), Vertex AI, or Airflow * Understanding of pipeline component design, DAG orchestration, and caching strategies * Ability to integrate data validation, model training, and deployment steps into workflows * Experience with pipeline parameterization and configuration management Software Engineering * Strong Python proficiency with production-grade coding standards * Ability to read, refactor, and extend existing codebases * Version control experience (Git) and structured change management * Familiarity with testing frameworks (pytest), dependency management (Poetry/UV), and code quality tools (pre-commit, linting) Cloud & Infrastructure * Hands-on experience with GCP (Vertex AI, Cloud Storage) or equivalent cloud platforms * Familiarity with containerization (Docker) and container orchestration (Kubernetes) * Experience with CI/CD pipelines for ML workflows * Understanding of secrets management and environment configuration Technical Skills: Nice to Have * Experience with Ray for distributed ML training and inference * Exposure to Hadoop ecosystem tools (Hive, HDFS, Spark on YARN) * Knowledge of ML model monitoring and drift detection * Experience with infrastructure-as-code (Terraform, Cloud Deployment Manager) * Familiarity with retail, supply chain, or demand forecasting domains * Experience working with data science teams to productionize research code * Background in scaling ML systems from prototype to enterprise-grade deployments, Analysis Skills, Apache Hadoop, Apache Hive, Apache Spark, Artificial Intelligence (AI), Bridge Building, Caching, Change Management, Civil Engineering, Cloud Computing, Configuration Management, Continuous Deployment/Delivery, Continuous Integration, Customer Experience, Data Formats, Data Management, Data Modeling, Data Processing, Data Quality, Data Science, Data Sets, Database Design, Database Technology, Demand Forecasting/Planning, Docker, Ecosystems, Engineering, Forecasting, Git, HDFS (Hadoop Distributed File System), Machine Learning, Model Validation, Network Connectivity, Operations Planning, Order/Customer Fulfillment, Performance Tuning/Optimization, Production Systems, Prototyping, Pytest, Refactoring, Retail, Software Engineering, Source Code/Configuration Management (SCM), Supply Chain, Team Player ## Description This role supports the development and modernization of the demand forecasting capabilities within Client's digital fulfillment organization. The team is responsible for forecasting order volumes, units, and fulfillment capacity across multiple channels (OPU, Ship-to-Home, Drive Up) to optimize store operations planning. Working closely with data scientists and platform engineers, this role bridges ML research and production by scaling data processing workloads, building robust ML pipelines, and ensuring forecasting models run reliably at scale. The ideal candidate brings an ML engineering mindset-combining data engineering, pipeline orchestration, and software engineering skills-to modernize a complex forecasting ecosystem that directly impacts store labor planning and customer experience. Employee Benefits: At LanceSoft, full time regular employees who work a minimum of 30 hours a week or more are entitled to the following benefits: * Four options of medical Insurance * Dental and Vision Insurance * 401k Contributions * Critical Illness Insurance * Voluntary Permanent Life Insurance * Accident Insurance * Other Employee Perks ## Related Videos - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Docker build without Docker](https://www.wearedevelopers.com/videos/100114-docker-build-without-docker) - [Git for Code Reviews](https://www.wearedevelopers.com/videos/429-git-for-code-reviews) - [DevOps for Machine Learning](https://www.wearedevelopers.com/videos/179-devops-for-machine-learning) ## Related Articles - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know)