Data Scientist

Description

The Data Scientist designs, develops, and implements advanced analytics, artificial intelligence (AI), and machine learning solutions that improve business decision‑making and operational performance. The Data Scientist partners with business stakeholders, Business Intelligence professionals, and Data Engineering teams to translate business requirements into scalable analytical solutions. The role develops and maintains data assets, advanced analytical models, AI solutions, and supporting data transformations while establishing technical standards and best practices for analytics and AI development.

Essential Functions

• Demonstrate alignment with the company's mission and core business values; collaborate with key internal/external resources; participate in ongoing self‑development.
• Model, promote, reinforce, and reward the consistent use of HOLT’s Values Based Leadership (VBL) tools, models, and processes to ensure alignment with Vision, Values, and Mission.
• Collaborate with leadership and business units to identify high‑value opportunities for advanced analytics, AI, machine learning, and governed semantic solutions.
• Design, develop, evaluate, deploy, and monitor statistical, predictive, prescriptive, machine learning, and AI solutions that address business opportunities and operational challenges.
• Develop AI solutions using structured and unstructured enterprise data and evaluate emerging AI technologies for responsible integration into business processes and analytical products.
• Establish standards and best practices for AI development, evaluation, documentation, deployment, monitoring, explainability, and responsible use.
• Design, develop, deploy, and maintain enterprise semantic models within Microsoft Fabric.
• Transform governed Gold‑layer data into analytics‑ready semantic models by defining dimensional structures, relationships, measures, calculations, hierarchies, metadata, and business context.
• Partner with Data Engineering teams to define and validate Gold‑layer requirements necessary to support semantic models and downstream analytical solutions.
• Ensure semantic models provide consistent business definitions, calculations, and context across Power BI, Microsoft Fabric, AI solutions, and other downstream consumers.
• Optimize semantic models for performance, scalability, usability, security, and maintainability.
• Implement modern development lifecycle practices for semantic models and analytical solutions, including Git‑based source control, versioning, testing, peer review, CI/CD, and deployment across development, test, and production environments.
• Partner with Business Intelligence professionals to ensure semantic models and analytical solutions effectively support enterprise reporting, self‑service analytics, AI applications, and business decision‑making.
• Extract, analyze, identify, and assess qualitative and quantitative patterns, trends, anomalies, and relationships from large and complex data sets.
• Prepare technical documentation and communicate analytical findings, model behavior, data requirements, and solution design to technical and business audiences.
• Provide training and support and serve as a technical expert for semantic modeling, advanced analytics, and AI throughout the organization.
• Maintain professional and technical knowledge by attending events, webinars, and educational opportunities.
• Work safely at all times and adhere to all applicable safety policies; comply with all company policies, procedures, and standards.
• Perform other duties as assigned.

Knowledge, Skills, and Abilities

• Advanced knowledge of data science, statistical analysis, machine learning, artificial intelligence, and advanced analytical methods.
• Strong knowledge of Microsoft Fabric architecture and development, including semantic models, notebooks, and related analytical capabilities.
• Advanced proficiency with SQL and Python for data transformation, analysis, modeling, and solution development.
• Strong knowledge of Power BI semantic models and DAX, including model performance optimization and reusable measure design.
• Working knowledge of generative AI, large language model concepts, conversational analytics, and enterprise AI solution patterns.
• Experience with Git‑based source control, Azure DevOps, pull requests, CI/CD, testing, and deployment practices.
• Knowledge of data governance, lineage, metadata management, documentation, data quality, and analytical security concepts including row‑level and object‑level security.
• Ability to identify complex problems, evaluate alternatives, and design scalable analytical and technical solutions.
• Excellent verbal, written, communication, presentation, interpersonal, and customer service skills, with the ability to convey complex technical and analytical concepts to both technical and non‑technical audiences.