Data Scientist

Red Hat•Raleigh, NC

9d•Remote

About The Position

This is a telecommuting role to be performed anywhere in the U.S. The Data Scientist will 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. The role involves designing, implementing, and optimizing 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. It also includes developing and managing 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. The position requires defining and applying 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. Additionally, the role involves designing and developing analytical dashboards in Tableau and interactive prototypes in Streamlit to visualize model outputs, KPIs, and experimental results for stakeholders. Communication of AI/ML methodologies, model behavior, system limitations, and analytical findings to executive, technical, and business stakeholders, translating quantitative results into actionable recommendations is key. The Data Scientist will 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. Analysis of production data trends, system telemetry, performance drift indicators, and outcome metrics to identify relationships and external factors affecting AI system outputs and business impact is also part of the role. The position leads 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. Application of 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 is required. Reviewing scientific literature and emerging AI research to evaluate and incorporate advanced modeling methodologies into enterprise AI system development is expected. Formulating, documenting, and recommending data-driven AI solutions aligned with operational and revenue objectives, supported by quantitative evidence and system performance metrics, is also a responsibility. Overseeing 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 is crucial. Developing production-grade AI/ML applications in Python, building APIs for model serving, managing model registries, implementing containerized deployments using Docker or Podman, and deploying systems on Kubernetes and OpenShift environments are also key aspects of the role.

Requirements

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.
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.
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.

Responsibilities

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.