Data Scientist

*Telecommuting role to be performed anywhere in the U.S. Analyze and process large-scale structured and unstructured datasets using SQL tools (PostgreSQL, PL/SQL, Spark SQL), API integrations (including Google Suite APIs), and automated preprocessing workflows to prepare data for advanced statistical and machine learning model development. What You Will Do: Design, implement, and optimize predictive and statistical models using Gradient Boosting frameworks (XGBoost, LightGBM, CatBoost) and Bayesian modeling (PyMC), applying feature selection and high-dimensional modeling techniques to enterprise marketing use cases. Develop and manage automated data transformation, cleansing, validation, and preprocessing workflows within MLOps frameworks (GitLab, Kubeflow, MLflow), ensuring data integrity, reproducibility, and CI/CD integration for ML systems. Define and apply statistical evaluation metrics, loss functions, performance KPIs, cross-validation techniques, and drift detection mechanisms to compare, test, and optimize AI/ML model accuracy, robustness, and reliability. Design and develop analytical dashboards in Tableau and interactive prototypes in Streamlit to visualize model outputs, KPIs, and experimental results for stakeholders. Communicate AI/ML methodologies, model behavior, system limitations, and analytical findings to executive, technical, and business stakeholders, translating quantitative results into actionable recommendations. Translate ambiguous business and analytical challenges into formal technical specifications and AI product roadmaps, conduct structured problem decomposition, and manage execution of AI feature backlogs using JIRA to coordinate iterative codesign and testing sessions with cross-functional data engineering, analytics, and business stakeholders. Analyze production data trends, system telemetry, performance drift indicators, and outcome metrics to identify relationships and external factors affecting AI system outputs and business impact. Lead strategic AI solution planning and prioritization within agile development frameworks, evaluating technical complexity, computational constraints, and measurable business impact to support marketing enterprise decision-making. Apply statistical theory, machine learning algorithms, NLP and Transformer-based architectures (NLTK, gensim, spaCy), and reinforcement learning frameworks (Gymnasium, Ray) to design and oversee the lifecycle of enterprise AI/ML systems from requirements through deployment and post-release monitoring. Review scientific literature and emerging AI research to evaluate and incorporate advanced modeling methodologies into enterprise AI system development. Formulate, document, and recommend data-driven AI solutions aligned with operational and revenue objectives, supported by quantitative evidence and system performance metrics. Oversee model training, cross-validation, recalibration, drift mitigation, and continuous improvement processes, including model registry management and production monitoring, to ensure predictive accuracy and long-term system stability. Develop production-grade AI/ML applications in Python, build APIs for model serving, manage model registries, implement containerized deployments using Docker or Podman, and deploy systems on Kubernetes and OpenShift environments. What You Will Bring: Master's degree (U.S. or foreign equivalent) in Computer Science, Information Systems, Information Management or related field and two (2) years of experience in the job offered or related role OR Bachelor's degree (U.S. or foreign equivalent) in Computer Science, Information Systems, Information Management or related field and four (4) years of experience in the job offered or related role. Must have two (2) years of experience with: independently architecting and developing end-to-end AI or machine learning applications, including translating ambiguous business requirements into technical specifications; designing UI/UX workflows and dashboards using tools (Pencil, Tableau or similar), building interactive web application prototypes using Streamlit, and engineering scalable back-end model-serving infrastructure; leading AI product lifecycle from ideation to deployment, including defining technical roadmaps, prioritizing AI/ML feature backlogs using data-driven frameworks, and coordinating iterative development sprints across engineering, data science, and business teams using JIRA; applying Natural Language Processing (NLP) and Deep Learning methodologies utilizing Transformer architectures, Transfer Learning, and Semantic Search/Information Retrieval, using Python libraries including NLTK, gensim, and spaCy; applying Advanced Modeling & Statistical Inference methodologies to develop predictive models using Gradient Boosting frameworks (XGBoost, LightGBM, CatBoost), Bayesian statistical modeling (PyMC), and Graph Neural Networks (PyTorch Geometric); applying Reinforcement Learning methodologies to design and optimize autonomous decision-making systems, including developing custom simulation environments using Gymnasium and executing distributed training workflows using Ray; hands-on development using Python (Scikit-learn, PyTorch, Tensorflow) to build AI solutions, including integrating external data and services via APIs including Google Suite APIs; utilizing NoSQL or high-dimensional data stores and performing extensive SQL database management, utilizing multiple SQL dialects, specifically PostgreSQL, PL/SQL (Oracle), and Spark SQL to query complex datasets for AI solutions; operationalizing and scaling machine learning models through automated pipelines and model versioning using GitLab or GitHub and open-source frameworks (Kubeflow/MLflow), including utilizing containerization tools (Docker or Podman) to deploy models on container orchestration platforms including Kubernetes and OpenShift; communicating and presenting complex AI concepts, model performance, and product value to both technical (engineering) and non-technical (executive) audiences using data visualization platforms including Tableau; developing data science and AI solutions within a B2B technology marketing or software industry context; and researching, evaluating, and prototyping novel AI methodologies, including new model architectures from academic papers, emerging Deep Learning frameworks, and advanced information retrieval technologies, and integrating them into production-level business solutions. #LI-DNI The salary range for this position is $125,000 - $135,000 /year. Actual offer will be based on your qualifications. Pay Transparency Red Hat determines compensation based on several factors including but not limited to job location, experience, applicable skills and training, external market value, and internal pay equity. Annual salary is one component of Red Hat’s compensation package. This position may also be eligible for bonus, commission, and/or equity. For positions with Remote-US locations, the actual salary range for the position may differ based on location but will be commensurate with job duties and relevant work experience. About Red Hat Red Hat is the world’s leading provider of enterprise open source software solutions, using a community-powered approach to deliver high-performing Linux, cloud, container, and Kubernetes technologies. Spread across 40+ countries, our associates work flexibly across work environments, from in-office, to office-flex, to fully remote, depending on the requirements of their role. Red Hatters are encouraged to bring their best ideas, no matter their title or tenure. We're a leader in open source because of our open and inclusive environment. We hire creative, passionate people ready to contribute their ideas, help solve complex problems, and make an impact. Inclusion at Red Hat Red Hat’s culture is built on the open source principles of transparency, collaboration, and inclusion, where the best ideas can come from anywhere and anyon