Data Scientist

Company Overview

Husky Technologies is a leading innovator dedicated to delivering high-quality technology products and services. With a strong foundation built on innovation, close client relationships, and a unique culture, Husky focuses on speed, flexibility, and productivity to maintain competitive advantage. The company offers significant career growth and the chance to work alongside passionate, creative professionals driving constant transformation and innovation.

Job Purpose

The Data Scientist role within Husky's global Enterprise Data & Analytics team involves converting enterprise data into actionable insights, predictive models, and AI-driven solutions. This position collaborates with business stakeholders, data engineers, architects, and analysts to design and deploy advanced analytics, machine learning models, and enterprise reporting solutions using modern cloud platforms such as Azure, Databricks, Microsoft Fabric, and Power BI.

Key Responsibilities

• Identify opportunities and challenges where advanced analytics, machine learning, and AI can add value.
• Design, develop, validate, and implement predictive, prescriptive, forecasting, anomaly detection, optimization, and classification models.
• Apply statistical and machine learning methods to resolve complex business problems and create reusable analytical assets.
• Prepare, transform, and profile enterprise data, developing pipelines with Azure Data Factory, Databricks, Microsoft Fabric, and SQL tools.
• Support design and implementation of Enterprise Data Vault, analytical data models, and data integration across ERP, CRM, Manufacturing, IoT, and external sources.
• Create dashboards and executive reporting using Power BI, translating complex findings into business recommendations, and collaborating with stakeholders on KPIs and reporting needs.
• Deploy and monitor machine learning models in production environments, ensuring governance, explainability, and reproducibility through MLOps tools like Databricks MLflow, Azure Machine Learning, Git, and CI/CD pipelines.
• Contribute to Enterprise Data Management strategies, including governance, data quality, metadata, and master data management.
• Engage with stakeholders and project teams to gather requirements, support delivery, and promote enterprise analytics and digital transformation.

Success Metrics

• Percentage of analytics projects completed on time and within scope.
• Number of production-ready analytics solutions delivered annually.
• Reduction of manual reporting through automation.
• Improvements in data quality scores and decreased data-related incidents.
• Accuracy and completeness of analytic datasets.
• Business benefits realized through analytics and adoption of dashboards/tools.
• Performance metrics for predictive models and machine learning deployments.
• Stakeholder satisfaction and reductions in reporting turnaround times.

Qualifications

• Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, Information Systems, or a related field; Master’s degree is a plus.
• 5-10 years of experience in Data Science, Analytics, Data Engineering, Business Intelligence, or Enterprise Data Management.
• Experience delivering analytics solutions in manufacturing, industrial, or global enterprises and working with cross-functional international teams.

Technical Expertise

• Proficiency in Python, R, SQL, machine learning, statistical analysis, predictive modeling, feature engineering, and AI/Generative AI technologies.
• Hands-on experience with Microsoft Azure ecosystem: Azure Data Lake Storage, Azure Data Factory, Azure Synapse Analytics, Microsoft Fabric, Azure Machine Learning.
• Expertise with Databricks Lakehouse platform components including Workflows, Delta Lake, Unity Catalog, MLflow, PySpark, Spark SQL, and Structured Streaming.
• Business intelligence tools like Power BI (including Power BI Service and DAX), and optionally Tableau and semantic modeling.
• Familiarity with database technologies: SQL Server, Oracle, Azure SQL, and optionally Snowflake and Fabric Warehouse.
• Knowledge of data architecture and governance frameworks: Data Vault 2.0, Kimball Dimensional Modeling, Enterprise Data Warehousing, Data Governance, Data Quality Management, Master Data Management, Metadata Management.
• Experience with DevOps and DataOps tools and methodologies: Git, Azure DevOps, CI/CD pipelines, Infrastructure as Code, Agile and Scrum practices.

Additional Information

Husky Technologies offers competitive compensation, growth opportunities, values diversity and inclusivity, and maintains a safe work environment. Employment is contingent upon successful background checks. The company requests no agency or phone inquiries.