Senior Data Analyst

Extend Enterprises•New York City, NY

About The Position

The Senior Data Analyst is a high-impact individual contributor. The ideal candidate combines strong business acumen, analytical rigor, and exceptional communication skills to transform complex data into actionable business insights. This role owns core business analytics while applying advanced methods to evaluate trends, validate hypotheses, and guide strategic decisions. As a strategic analytics partner, this individual will help leaders identify opportunities, evaluate business performance, measure the impact of key initiatives, and guide decisions through thoughtful analysis. Beyond reporting what happened, they will uncover why it happened, assess confidence in the findings, and recommend practical actions that drive business outcomes. This role is ideal for someone who enjoys solving business problems, influencing decision-making, and serving as a trusted advisor across Product, Business Development, Operations, Finance, and Executive Leadership.

Requirements

5+ years of experience in analytics, data science, or statistics (ideally in SaaS/ tech)
Advanced SQL and strong experience working with large datasets; strong data validation and analytical rigor
Excellent business acumen: consistently translates ambiguous questions into measurable definitions, prioritizes work by impact, and ties analysis to decisions and outcomes.
Proficiency in statistical analysis and experimentation (hypothesis testing, confidence intervals, regression analysis, variance analysis, design and analyzing of A/B testing), clear distinction between correlation vs causation; experience with Python, R, or similar for statistical modeling and advanced analysis.
Strong understanding of SaaS metrics (ARR, retention, churn, LTV, CAC, product adoption) and how to connect them to product and growth levers.
Executive-ready communication and stakeholder partnership: comfortable explaining uncertainty/tradeoffs and influencing senior leadership; proactive, high-ownership operating style.
High ownership / proactive execution: independently identifies opportunities, drives projects end-to-end, and communicates progress without needing heavy direction.
Understanding of data privacy and responsible AI practices.

Nice To Haves

Experience with predictive modeling is a plus but not required.

Responsibilities

Own Core Business Analytics: Define, maintain, and continuously improve dashboards and reporting for revenue, SaaS performance, customer growth, and product adoption. Establish clear metric definitions and governance to ensure consistency and integrity across teams. Serve as the trusted source of truth for core business KPIs.
Statistical Analysis & Experimental Rigor: Translate complex data into clear narratives and recommendations for leadership. Apply statistical methods to evaluate trends, cohort performance, and business experiments. Assess sample size, variance, and statistical significance when interpreting business outcomes. Distinguish signal from noise and clearly communicate confidence levels in findings. Design and evaluate A/B tests and other controlled experiments in partnership with Product and BD. Provide structured guidance on when data is sufficient to inform decisions, and when it is not.
Advanced Analytics & Forecasting: Conduct advanced analyses to identify drivers of customer retention, churn, product adoption, revenue growth, and business performance. Support forecasting and scenario planning initiatives. Identify leading indicators, emerging trends, and potential business risks. Experience with predictive modeling is a plus but not required.
AI-Enabled Analytics & Automation: Leverage AI tools to accelerate analysis, enhance modeling workflows, and surface deeper insights. Experiment with AI-assisted anomaly detection, forecasting support, and exploratory data analysis. Identify opportunities to automate recurring analytical tasks using scripting or AI-enabled tools. Promote responsible and secure use of AI in analytics.
Data Quality & Infrastructure Collaboration: Monitor data integrity and investigate anomalies. Partner with Engineering to improve data pipelines and resolve discrepancies. Contribute to building scalable, durable analytics infrastructure.