Data Scientist

General Summary of Job Responsibilities

Essential Duties and Responsibilities

• Prioritize business initiatives and structure projects for optimal success, considering budget, staffing, and regulatory requirements. 
• Communicate data science outputs effectively and support the development of models and solutions that provide clear, actionable insights for business execution. 
• Plan, organize, and manage resources and processes to achieve project or program objectives within established scope, timelines, quality standards, and budget constraints. 
• Lead analytics initiatives from ideation through production and adoption, demonstrating a strong understanding of the analytics lifecycle and common pitfalls. 
• Perform other duties as assigned or as necessary. 

Additional Essential Duties and Responsibilities

• Design, develop, and maintain scalable analytical solutions using Python, SQL, Spark, Databricks, Microsoft Fabric, and cloud-based analytics platforms.
• Build and optimize enterprise data pipelines that extract, transform, and integrate data from multiple internal and external sources.
• Design and implement Lakehouse architectures, dimensional models, and curated data products to support advanced analytics and reporting.
• Develop predictive, prescriptive, and machine learning models to identify trends, risks, opportunities, and future business outcomes.
• Independently perform data exploration, feature engineering, model development, validation, deployment, and monitoring.
• Translate ambiguous business problems into analytical frameworks, hypotheses, models, and actionable recommendations.
• Develop complex business logic, algorithms, and statistical methodologies to support decision-making.
• Create, optimize, and maintain Power BI semantic models, dashboards, reports, KPIs, and data visualizations.
• Partner with business leaders to identify opportunities where analytics, machine learning, and AI can improve operational performance and business outcomes.
• Evaluate data quality, establish governance practices, and ensure analytical solutions maintain accuracy, scalability, and reliability.
• Present analytical findings and recommendations to both technical and executive audiences.
• Support the deployment, operationalization, and continuous improvement of analytical models and decision-support tools.

Knowledge/Skills/Abilities

• Strong quantitative analytics skills and structured problem solving abilities. 
• Ability to evaluate strengths and weaknesses of alternative solutions, conclusions, or approaches. 
• Basic knowledge of data modeling, machine learning algorithms, statistical analysis, data visualization, and data engineering. 
• Broad understanding of project management principles and ability to identify project and business requirements. 
• Excellent written and verbal communication skills. 
• Ability to compile, organize, interpret, and clearly communicate data and analytical results. 
• Strong process management skills. 
• Ability to use logic and reasoning to evaluate alternative solutions, conclusions, or approaches. 

Additional Knowledge/Skills/Abilities

• Advanced proficiency in Python, SQL, R, PySpark, or similar analytical programming languages.
• Deep experience with Microsoft Fabric, Databricks, Azure Data Services, Data Lakehouse architectures, and enterprise data platforms.
• Strong understanding of data engineering concepts including ETL/ELT, data orchestration, pipeline development, and distributed data processing.
• Experience building semantic models and interactive dashboards using Power BI.
• Demonstrated expertise in predictive analytics, forecasting, statistical modeling, machine learning, and AI applications.
• Strong knowledge of dimensional data modeling, star schema design, data warehousing, and Lakehouse principles.
• Experience combining structured and unstructured data sources to create enterprise analytical solutions.
• Ability to independently lead analytics projects from business intake through production deployment.
• Strong critical thinking and problem-solving skills with the ability to translate business challenges into technical solutions.
• Ability to communicate complex technical concepts to non-technical stakeholders and executive leadership.

Education/Experience

• Bachelor’s degree in Information Technology, Data Analytics, or a related field, with two (2) or more years of experience in data science, data analysis, data modeling, and business needs assessment 
  • [OR] Associate’s degree in Data Science, Information Technology, Data Analysis, or a related field, with four (4) or more years of relevant experience 
  • [OR] High School Diploma with six (6) or more years of experience in data science, data analysis, data modeling, and business needs assessment 

Preferred Experience

Candidates with one or more of the following skills will stand out: 

• Hands-on experience with Microsoft Fabric including Data Factory, Data Engineering, Data Science, Lakehouse, and Power BI workloads.
• Experience developing enterprise-scale machine learning and predictive analytics solutions.
• Expertise in building and deploying data products that support operational decision-making.
• Experience creating feature stores, model pipelines, and MLOps frameworks.
• Familiarity with geospatial analytics, optimization techniques, causal inference, time-series forecasting, and statistical experimentation.
• Experience integrating operational, financial, asset, customer, and third-party datasets to generate business insights.
• Demonstrated success working independently while managing multiple analytics initiatives simultaneously.
• Experience supporting utility, energy, infrastructure, asset management, safety, supply chain, or operational analytics environments.