**Overview** ------------ The Data Scientist 4 is responsible for designing, developing, and delivering advanced machine\-learning and AI\-driven software capabilities that power Hyland’s commercial products and platforms. This role blends deep engineering expertise with applied data science, enabling scalable features, intelligent automation, and production\-ready ML systems. **Responsibilities** -------------------- * Design and implement robust, scalable machine\-learning services and components for inclusion in customer\-facing software. * Build efficient data pipelines to collect, clean, normalize, and transform structured and unstructured data used by ML features. * Architect and implement model\-training workflows, model\-versioning strategies, and evaluation pipelines for ongoing improvement. * Conduct exploratory data analysis (EDA), feature engineering, and statistical evaluations to support model development. * Apply advanced statistical methods, A/B testing frameworks, and hypothesis\-driven experimentation to validate model performance. * Prototype and evaluate new machine\-learning models, deep\-learning architectures, and embeddings strategies aligned to product needs. * Develop predictive, generative, and analytical models that enable automation, forecasting, classification, clustering, recommendations, or other product capabilities. * Optimize models for performance, cost, latency, and scalability across CPU/GPU environments. * Stay informed on the latest advancements in AI, ML, LLMs, vector databases, and retrieval frameworks; transform them into real\-world product features. * Develop clear internal documentation on model behavior, data flows, architectural decisions, and operational considerations. * Establish and evolve engineering standards for ML/AI development, including testing strategies, monitoring, observability, and reliability. * Contribute to a shared knowledge base of best practices for ML engineering and applied data science across the organization. * Operate as a technical expert and trusted advisor to product engineering teams, helping shape AI feature roadmaps and implementation strategies. * Communicate complex statistical or modeling concepts to engineers, architects, and product leaders in clear, actionable ways. * Provide mentorship and technical guidance to junior team members and help strengthen the organization’s AI engineering maturity. **Basic Qualifications** ------------------------ * Bachelor’s degree in Computer Science, Engineering, Mathematics, or related technical discipline (or equivalent experience). * Significant experience developing machine\-learning or AI\-based software systems in production environments. * Mastery of Python and applied machine learning libraries (TensorFlow, PyTorch, scikit\-learn, Pandas). * Deep understanding of statistical modeling, hypothesis testing, probability theory, and mathematical optimization. * Demonstrated expertise with relational, NoSQL, big\-data, or graph databases, with strong ability to architect data structures for ML workloads. * Experience building and deploying APIs, microservices, or distributed systems that run ML inference at scale. * Strong experience with data visualization and model\-explainability tools (Jupyter, Tableau, Plotly, or equivalent). * Ability to articulate complex technical concepts clearly in both written and verbal communication. * Strong critical\-thinking and analytical problem\-solving abilities. * Experience mentoring or supporting developing engineers or data scientists. * Up to 5% travel required.