Lead Data Scientist

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.

• 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.

• 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.