Analyst - Environmental, Health & Safety

Job Description

Job Description

JOB SUMMARY

PODS runs on data. Across Environmental, Health & Safety, the organization is investing in analytics that identify risk earlier, using data to reduce incidents, control claims costs, and strengthen compliance performance. Operations Data Science & AI is a new team built to turn operational expertise into automated, optimized decision systems. This role sits across both.

As an Analyst II, you will report to the Director, Operations Data Science & AI and split your time roughly evenly between two areas. About half of your work will support Safety, Workers' Compensation, Risk Management, and Compliance. That means analyzing incident and claims data, maintaining the reporting leaders rely on, and surfacing the trends and root causes that drive risk reduction. The other half will support the Operations Data Science & AI team, where you will prepare and validate data, automate recurring analysis, and build the tools that put results in front of the people making operational decisions.

This is a hands-on building role. You will write SQL and Python every day, and the work is measured by whether the analysis and tools you build get used. You will partner with experienced safety, risk, and operations leaders, and you will be expected to learn both domains quickly.

ESSENTIAL DUTIES AND RESPONSIBILITIES

• Analyze safety, risk, and compliance data:

◦ Analyze incident, claims, and operational data across Safety, Workers’ Compensation, Risk Management, and Compliance to identify trends, root causes, emerging risks, and areas requiring targeted intervention.

◦ Conduct cost and trend analysis related to Workers’ Compensation and Risk Management to identify cost drivers and opportunities for reduction.

◦ Track and report key performance indicators, including leading and lagging indicators and the effectiveness of safety and risk initiatives.

• Build reporting and decision tools:

◦ Develop and maintain dashboards, reports, and interactive analytical tools that provide visibility into key metrics such as recordable injuries, vehicle incidents, claims costs, and compliance performance.

◦ Work directly with stakeholders to understand how decisions actually get made, then build tools that fit those workflows and improve them based on feedback and observed usage.

◦ Support executive-level reporting, including weekly and monthly trend analysis, insights, and recommended actions.

• Support Operations Data Science & AI:

◦ Partner with data scientists and engineers on data preparation, validation, feature development, and analysis supporting forecasting, optimization, and automation work.

◦ Build reproducible pipelines that automate recurring analyses and reporting, replacing manual processes.

◦ Support development of predictive and proactive analytics that identify potential safety and risk exposures after incidents occur.

• Improve the data foundation:

◦ Help standardize data definitions, reporting methodologies, and metrics across Safety, Risk, Compliance, and Operations.

◦ Identify data gaps and partner with cross-functional teams to improve data quality, accessibility, and reporting accuracy.

◦ Collaborate with IT, Finance, HR, and Operations to integrate data sources and expand analytical capabilities.

• Document and communicate clearly:

◦ Translate analysis into clear, actionable recommendations for EHS, Risk, and Operations stakeholders.

◦ Support continuous improvement initiatives by providing data-driven insight into operational and safety performance.

◦ Evaluate the effectiveness of implemented initiatives by analyzing performance data and recommending adjustments.

◦ Document methodology, assumptions, and limitations alongside every analysis and tool you build, so the work is transparent and repeatable.

JOB QUALIFICATIONS: Essential Skills, Abilities and Example Behavior(s)

• SQL fluency: Strong SQL skills, including joining, aggregating, and validating data across multiple source systems.

• Python for analysis: Working proficiency in Python for data analysis and for building analytical tools, using libraries such as pandas.

• Rapid tool development: Ability to move quickly from question to working solution using lightweight frameworks, reusable components, and modern development tools, including AI-assisted development tools where appropriate.

• Data visualization: Ability to turn complex data into clear, intuitive visualizations and interfaces that help people make decisions.

• Analytical judgment: Ability to recognize when a result looks wrong, investigate why, and stand behind the numbers you publish.

• Cross-functional thinking: Ability to connect data across Safety, Risk, Compliance, and Operations to identify meaningful trends and business impact.

• Communication and documentation: Ability to explain analysis, methods, and limitations in plain language to non-technical audiences, and to document work so that others can maintain it.

• Learning orientation: Demonstrated ability to pick up new domains, tools, and techniques quickly with limited hand-holding.

• Structure amid ambiguity: Ability to take a loosely defined question, determine what is needed, and move it to a practical answer.

• Attention to detail: High attention to detail with a consistent focus on data accuracy and integrity.

JOB QUALIFICATIONS: Education & Experience Requirements  

• Bachelor’s degree in a quantitative or analytical field such as Statistics, Mathematics, Economics, Engineering, Physics, Public Health or Epidemiology, Data Science, Operations Research, Computer Science, Information Systems, Geography/GIS, Finance, or a related field required; equivalent education and experience will be considered.

• 2+ years of experience in an analyst, data analysis, or quantitative research role. Relevant internship, co-op, or graduate research may count toward experience.

• Hands-on experience with SQL and Python, demonstrated through prior work, academic research, or a portfolio of personal or open-source projects.

• Proficiency in Microsoft Excel.

• Experience building dashboards or interactive data applications preferred; experience with Power BI, Tableau, or a similar business intelligence tool preferred.

• Experience with statistical analysis, forecasting, or predictive modeling preferred.

• Experience with a cloud data warehouse (Snowflake preferred) is a plus.

• Experience supporting safety, insurance or claims, healthcare, logistics, transportation, or another operationally complex business is a plus.

• Experience using AI-assisted tools to accelerate analysis, development, and documentation is a plus.