The application window is expected to close on: 08/22/2026
Job posting may be removed earlier if the position is filled or if a sufficient number of applications are received.
Meet the TeamWe are looking for an early in career Data Science Analyst to help turn product, customer, operational, and business data into clear insights that support better decision-making across Enterprise Security. This is an opportunity to work with multi-functional teams, learn about cybersecurity and SaaS product analytics, and contribute to data-driven decisions that improve product quality, customer outcomes, operational efficiency, and business visibility.
Your ImpactAs a Data Science Analyst, you will partner with Product, Engineering, Quality, Finance, and Operations teams to collect, analyze, and interpret data from multiple sources. You will help build dashboards, prepare recurring reports, identify trends, and translate findings into clear insights for technical and non-technical collaborators. In this role, you will gain exposure to cybersecurity, enterprise SaaS, product analytics, financial and operational metrics, and cross-functional decision-making. Your work will help teams better understand product usage, customer adoption, operational performance, cost trends, and opportunities to improve how we deliver value to customers. You will have the opportunity to grow your analytical, technical, and business skills while contributing to high-impact initiatives across the Enterprise Security organization.
Key Responsibilities- Collect, clean, validate, and analyze data from multiple sources, including product telemetry, usage data, customer data, operational metrics, and business reports.
- Build and maintain dashboards, reports, and recurring analyses to track key product, quality, adoption, financial, and operational metrics.
- Support analysis of COGS and gross margin across customer cohorts and the overall business, helping identify trends, drivers, risks, and opportunities for improvement.
- Partner with Product, Engineering, Quality, Finance, and Operations teams to understand business questions and translate them into data-driven analysis.
- Help define and track metrics that measure product usage, customer adoption, quality, operational efficiency, and business outcomes.
- Analyze trends and summarize findings to support planning, forecasting, roadmap discussions, and business-impact measurement.
- Translate data findings into clear insights, summaries, and recommendations for both technical and non-technical audiences.
- Automate recurring reporting and data-analysis workflows using SQL, Python, R, or similar tools.
- Identify data-quality issues and work with partners to improve data accuracy, consistency, and usability.
- Maintain documentation of data sources, assumptions, calculations, dashboards, and analysis methods.
- Use AI-assisted tools responsibly to improve data analysis, summarization, automation, and reporting workflows.
Minimum Qualifications- Meet one of the following (completed within the past 3 years or expected to be completed within the next 12 months):
- A Master's degree with0 years of work experience; or
- A Bachelor's degree with 2 years of work experience.
- Qualifying education and work experience should be in Data Science, Computer Science, Engineering, Statistics, Mathematics, Business Analytics, Economics, or a related field.
- Experience using SQL for querying and validation, alongside competency in an analytical scripting language like Python or R.
- Experience designing dashboards and reports using industry-standard platforms such as Tableau, Power BI, or Splunk.
- Strong critical thinking and problem-solving skills required to navigate sophisticated data
Preferred Qualifications- Previous internship, research, or work experience in data/business analytics, with exposure to product, SaaS, or operational data environments.
- Familiarity with statistical modeling, forecasting, and machine learning, combined with the ability to lead large, sophisticated datasets and ensure data integrity.
- Proven success partnering with cross-functional teams and translating sophisticated technical analyses into clear, stakeholder-ready recommendations.
- Strong curiosity about the cybersecurity landscape, including enterprise security, AI, and modern data platforms.
