> Markdown version of [/jobs/ext/2305635-data-scientist-recsys](https://www.wearedevelopers.com/jobs/ext/2305635-data-scientist-recsys). 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). --- # Data Scientist - RecSys - **Company:** Oak/Leyden Developmental Services Inc - **Location:** United States (Remote available) - **Contract:** Permanent contract - **Skills:** A/B Testing, Amazon Web Services, Unit Testing, Microsoft Azure, Batch Processing, Big Data, BigQuery, Extract Transform Load (ETL), Data Transformation, Distributed Computing Environment, Data Flow Control, Apache Hadoop, Apache Hive, Python (Programming Language), PostgreSQL, Machine Learning, MongoDB, MySQL, Online Analytical Processing, NoSQL, NumPy, Online Transaction Processing, Recommender Systems, Tensorflow, Standard Sql, Data Streaming, Google Cloud, Data Ingestion, Azure Data Factory, Pytorch, Large Language Models, Snowflake, Apache Spark, Deep Learning, Pandas, Pytest, AI Platforms, Scikit Learn, Information Technology, Apache Flink, Cassandra, HuggingFace, AWS Glue, Apache Kafka, Machine Learning Operations, Presto, Data Pipelines, Docker, Amazon Redshift, Databricks - **Published:** August 30, 2026 - **Apply:** https://arc.dev/remote-jobs/j/redirect/pg4dq1tqa8 ## About the Role * Bachelor's or Master's degree in Computer Science, Engineering, or a related field * Strong Python experience with recent production use, including hands-on work with data science and machine learning libraries and frameworks (e.g., Pandas, Polars, NumPy, scikit-learn, PyTorch, TensorFlow, JAX, Hugging Face, …) * Experience building and deploying end-to-end machine learning systems on cloud AI platforms (Azure, GCP, or AWS), from ETL pipelines to deployment and monitoring, including model versioning and experiment tracking, supporting either batch or real-time workflows. * Strong understanding of deep learning-based recommender systems for next-item prediction, and analogous NLP architectures that model sequential patterns and context * Demonstrated experience building efficient data transformation pipelines for both transactional (OLTP) and analytical (OLAP) workloads, with strong knowledge of SQL and NoSQL databases (e.g., PostgreSQL, MySQL, Redshift, Snowflake, BigQuery, MongoDB, Cassandra) * Experience with unit and integration testing (e.g., Pytest), CI/CD pipelines, and Docker-based containerization What Will Set You Up Apart * Experience building large-scale recommender systems (e.g., candidate generation, ranking, retrieval, personalization). * Track record of publications in deep learning at relevant conferences or journals. * Experience with Azure Data Factory / AWS Glue / Google Cloud Dataflow. * Experience designing and analyzing A/B tests, with a solid understanding of relevant evaluation metrics. * Experience designing and implementing metadata-driven pipelines to scale automated A/B testing systems. * Experience developing multi-modal models that integrate multiple data types (e.g., text, images, audio). * Experience applying transformer-based models or large language models (LLMs) to recommendation or personalization tasks. * Experience with distributed training, including data parallelism and model parallelism. * Experience with distributed data processing and big data technologies (e.g., Spark, Hadoop, Flink, Kafka, Hive, Presto, Databricks). ## Description * Design, implement, and optimize end-to-end recommendation pipelines, from data ingestion to model inference. * Build and maintain scalable ETL pipelines to support reliable and efficient data flows. * Develop, evaluate, and continuously improve ML models for recommendation systems. * Research, prototype, and implement state-of-the-art (SOTA) approaches to improve recommendation quality and drive key business metrics. * Scale and optimize data and model pipelines to handle large volumes of data and real-time or batch processing needs. * Integrate multi-modal data (e.g., behavioral, transactional, and contextual signals) from various systems into recommendation models. * Ensure robustness and stability of pipelines by implementing unit and integration tests across data, modeling, and deployment workflows. * Monitor and maintain end-to-end system performance, including data pipelines, model quality, and downstream impact. * Design and analyze A/B tests to evaluate model performance and support data-driven product decisions. * Build dashboards and observability tools to track model metrics, system health, and business KPIs. * Collaborate closely with Data Engineers, Software Engineers, and stakeholders to deliver scalable, production-ready solutions. ## Related Videos - [MySQL Protocol Features You Should Be Aware Of](https://www.wearedevelopers.com/videos/100267-mysql-protocol-features-you-should-be-aware-of) - [Leveraging Real time data in FSIs](https://www.wearedevelopers.com/videos/806-leveraging-real-time-data-in-fsis) - [Vectorize all the things! 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