*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 anyone. When this is realized, it empowers people from different backgrounds, perspectives, and experiences to come together to share ideas, challenge the status quo, and drive innovation. Our aspiration is that everyone experiences this culture with equal opportunity and access, and that all voices are not only heard but also celebrated. We hope you will join our celebration, and we welcome and encourage applicants from all the beautiful dimensions that compose our global village.
Equal Opportunity Policy (EEO)
Red Hat is proud to be an equal opportunity workplace and an affirmative action employer. We review applications for employment without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, ancestry, citizenship, age, veteran status, genetic information, physical or mental disability, medical condition, marital status, or any other basis prohibited by law.
Red Hat does not seek or accept unsolicited resumes or CVs from recruitment agencies. We are not responsible for, and will not pay, any fees, commissions, or any other payment related to unsolicited resumes or CVs except as required in a written contract between Red Hat and the recruitment agency or party requesting payment of a fee.Red Hat supports individuals with disabilities and provides reasonable accommodations to job applicants. If you need assistance completing our online job application, email [email protected]. General inquiries, such as those regarding the status of a job application, will not receive a reply.
Skills Required
- Master's degree in Computer Science, Information Systems, Information Management, or a related field and two years of experience in the offered or related role; alternatively, a bachelor's degree in one of these fields and four years of experience.
- Two years of experience architecting and developing end-to-end AI or machine learning applications and translating ambiguous business requirements into technical specifications.
- Two years of experience designing UI/UX workflows and dashboards using Pencil, Tableau, or similar tools; building Streamlit prototypes; and engineering scalable model-serving infrastructure.
- Two years of experience leading AI product lifecycles, defining roadmaps, prioritizing feature backlogs, and coordinating development sprints using JIRA.
- Two years of experience applying NLP and deep learning with Transformer architectures, transfer learning, semantic search, and information retrieval using NLTK, gensim, and spaCy.
- Two years of experience developing predictive models using XGBoost, LightGBM, CatBoost, PyMC, and Graph Neural Networks with PyTorch Geometric.
- Two years of experience applying reinforcement learning, including Gymnasium simulation environments and distributed training with Ray.
- Hands-on Python development using scikit-learn, PyTorch, and TensorFlow, including API-based integration with external data and services such as Google Suite APIs.
- Experience using NoSQL or high-dimensional data stores and managing SQL databases with PostgreSQL, Oracle PL/SQL, and Spark SQL.
- Experience operationalizing and scaling machine learning models with GitLab or GitHub, Kubeflow or MLflow, Docker or Podman, Kubernetes, and OpenShift.
- Experience developing data science and AI solutions in a B2B technology marketing or software industry context.
- Experience researching, evaluating, and prototyping novel AI methodologies and integrating emerging models, frameworks, and information retrieval technologies into production solutions.
Red Hat Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Red Hat and has not been reviewed or approved by Red Hat.
-
Healthcare Strength — Healthcare coverage is presented as comprehensive, spanning medical, dental, and vision along with life and disability coverage. Access to HSA/FSA options and broadly positive reception of health benefits support the view that healthcare is a core strength.
-
Leave & Time Off Breadth — Time-off offerings are described as generous, with substantial PTO for new hires plus additional recharge days and an end-of-year shutdown for many non-critical roles. Paid volunteer time, holidays, sick days, and supportive expectations around taking time off reinforce the breadth of leave benefits.
-
Strong & Reliable Incentives — The rewards package includes performance bonuses and a recurring quarterly bonus program tied to company and individual performance. Availability of ESPP participation further adds to incentive pathways beyond base pay.
Red Hat Insights
What We Do
At Red Hat, we connect an innovative community of customers, partners, and contributors to deliver an open source stack of trusted, high-performing solutions. We offer cloud, Linux, middleware, storage, and virtualization technologies, together with award-winning global customer support, consulting, and implementation services. Red Hat is a rapidly growing company supporting more than 90% of Fortune 500 companies.
Why Work With Us
Red Hatters freely exchange different viewpoints, contribute ideas, and solve problems together. Our love of collaboration, accountability, a sense of community, and a measure of autonomy combine to create a powerful force that fosters innovation and makes Red Hat a great place to work.
Gallery
