Founded in 1972, Sealaska has been evolving from a land-management-focused company to a services-focused company with an emphasis on sustainable industries. There are three major business groups under the umbrella of Sealaska: Government Services, Natural Resources and Natural Foods:

• Sealaska Government Services is a full-service company with a worldwide reach in the areas of water and energy management, construction, data management and analytics.
• Sealaska Natural Resources group takes the balanced approach to achieve sustainable growth and long-term benefit for land management and economic development.
• We have been using a rigorous and intentional investment process to build a Natural Foods business group through acquisition, which has been highlighted as a key strategic initiative to achieve increased profitability.

The Senior Data Analyst will conduct research and perform data analysis including developing and validating statistical models, create dashboards, write analysis reports, and provide recommendations for improving data analysis and models for clients. Will combine large, disparate data sets to find opportunities to answer questions and provide insight for decision making.

Duties/Responsibilities

• Conduct and evaluate policy scenarios and detailed recommendations using data.
• Using critical thinking and the ability to interpret data requests, direct the collection and processing of data, analyze results, report results to users, prepare related planning and analytical documents.
• Develop research questions and approaches to answering them
• Work with client stakeholders to identify opportunities for leveraging data to drive solutions.
• Design and develop data collection procedures and acquisition of data and analysis for data analysis.
• Assess the effectiveness and accuracy of new data sources and data gathering techniques.
• Assist with data management, organization, extraction, and maintenance.
• Use research and statistical analysis to drive business decisions.
• Lead various teams to implement solutions and monitor outcomes in production environments.
• Develop processes and tools to monitor and analyze model performance and data accuracy.
• Prepare and present data to various audiences using tables, charts, and narrative reports in a way that stakeholders can easily understand.
• Ensure that documentation is produced/updated & available for project sustainability.
• Provide coding assistance to various teams.
• Present statistical analysis methods at conferences
• Other duties as assigned.

Required Skills/Abilities

• Must have strong experience managing data, using a variety of statistical analysis methods, and using a variety of data tools
• Comfortable working with a wide range of stakeholders and functional teams
• Must have a passion for discovering solutions hidden in large data sets and working with stakeholders to improve outcomes.
• Excellent verbal and written communication skills.
• Excellent interpersonal and customer service skills.
• Excellent organizational skills and attention to detail.
• Excellent time management skills with a proven ability to meet deadlines.
• Strong analytical and problem-solving skills.
• Ability to prioritize tasks and to delegate them when appropriate.
• Ability to function well in a high-paced and at times stressful environment.
• Proficient with Microsoft Office Suite or related software.
• Technical expertise including
• Coding knowledge and experience with statistical languages such as Python, R, MATLAB, or similar languages
• Experience with SQL and other data querying tools
• Knowledge and experience in statistical and data mining techniques
• Experience visualizing/presenting data for stakeholders using: Tableau, PowerBI, R Shiny, D3, ggplot, etc.

Preferred Qualification And Skills

• Knowledge of education or environmental data would be beneficial
• Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
• Technical expertise including
• Knowledge and experience in machine learning techniques such as Random Forest, Boosting, Trees, text mining, social network analysis, etc.
• Experience creating and using advanced machine learning algorithms and statistics: regression, simulation, scenario analysis, modeling, clustering, decision trees, neural networks, etc.

Education And Experience

• Research-based master’s degree or PhD in a relevant field (Applied Statistics, Computer Science, Machine Learning, Operational Research, Statistics, Mathematics, etc.)
• 5+ years of experience working in a data analyst role using statistical methods to solve business problems