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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Applied Data Scientist / Machine Learning Engineer - **Company:** Allied Llc - **Location:** United States (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** A/B Testing, Artificial Intelligence, Amazon Web Services, Data Analysis, Microsoft Azure, BigQuery, Software as a Service, Computer Programming, Information Engineering, Data Infrastructure, Python (Programming Language), Machine Learning, Routing, Recommender Systems, Tensorflow, Software Engineering, Supervised Learning, Google Cloud, Feature Engineering, Pytorch, Large Language Models, Snowflake, Generative AI, Containerization, Scikit Learn, Optimization Algorithms, Xgboost, Machine Learning Operations, Marketplace, Data Pipelines, Amazon Redshift, Databricks - **Published:** August 15, 2026 - **Apply:** https://www.dice.com/job-detail/c9e75c82-577f-4ee8-87a9-a88095a25c90 ## About the Role The ideal candidate has a strong foundation in applied machine learning, experience working with large-scale product data, and a proven track record of delivering production-ready ML solutions in SaaS environments., * 5+ years of experience in Applied Data Science, Machine Learning, AI, or ML Engineering. * Proven experience building, deploying, and maintaining machine learning models in production environments. * Strong programming skills in Python and advanced proficiency in SQL. * Hands-on experience with machine learning frameworks such as PyTorch, TensorFlow, Scikit-learn, XGBoost, or similar technologies. * Deep understanding of machine learning concepts including: + Supervised learning + Forecasting + Recommendation systems + Ranking algorithms + Optimization techniques + Statistical modeling + Experimentation frameworks * Experience working with large-scale, real-world datasets and solving complex product challenges. * Strong background in data engineering concepts, feature engineering, and data pipelines. * Experience partnering with software engineering teams to deploy, monitor, and improve production ML systems. * Familiarity with modern data platforms such as Databricks, Snowflake, BigQuery, Redshift, or similar. * Understanding of MLOps best practices including monitoring, model governance, feature stores, and automated retraining. * Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform (Google Cloud Platform). * Strong product mindset with the ability to connect machine learning initiatives to business objectives and customer outcomes. * Excellent communication and stakeholder management skills., * Experience building ML-powered SaaS products. * Experience with decision intelligence, workforce optimization, pricing, scheduling, route optimization, marketplace, or operational intelligence solutions. * Experience with LLMs, Generative AI, Retrieval-Augmented Generation (RAG), or agentic AI applications in production environments. * Experience designing and analyzing A/B tests, experimentation platforms, or causal inference frameworks. * Experience operating machine learning systems at scale with feedback loops and continuous improvement processes. * Previous experience as a Senior, Staff, or Lead Data Scientist, Applied Scientist, or Machine Learning Engineer. ## Description * Design, develop, and deploy machine learning models that power customer-facing products and business-critical decision-making. * Partner with Product, Engineering, and Analytics teams to identify opportunities where machine learning can drive measurable value. * Build and maintain scalable data and feature pipelines to support model training, deployment, and monitoring. * Develop solutions across forecasting, recommendation systems, optimization, ranking, customer behavior modeling, and predictive analytics. * Apply statistical modeling, experimentation, and causal inference techniques to solve complex business problems. * Evaluate model performance, interpret results, and communicate insights and trade-offs to technical and non-technical stakeholders. * Support MLOps initiatives including model versioning, orchestration, monitoring, drift detection, and retraining workflows. * Leverage cloud-native technologies and modern data platforms to deploy and scale machine learning systems. * Explore and implement LLMs, Generative AI, and agentic workflows to enhance product capabilities. ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Creating a routing app with Google Maps API from scratch](https://www.wearedevelopers.com/videos/831-creating-a-routing-app-with-google-maps-api-from-scratch) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [TikTok's Privacy Innovation](https://www.wearedevelopers.com/videos/1036-tiktok-s-privacy-innovation) - [Geometric deep learning for drug discovery](https://www.wearedevelopers.com/videos/264-geometric-deep-learning-for-drug-discovery) - [Empowering Retail Through Applied Machine Learning](https://www.wearedevelopers.com/videos/976-empowering-retail-through-applied-machine-learning) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [What Industries Outside of AI Are Hiring The Most AI Experts?](https://www.wearedevelopers.com/magazine/98-what-industries-outside-of-ai-are-hiring-the-most-ai-experts) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Got AI ideas but no money? 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