Senior Data Analyst

Extend Enterprises•New York, NY

3d•$140,000 - $170,000

About The Position

The Senior Data Analyst (Sr. Manager IC) serves as the company’s senior individual contributor for analytics, combining strong business acumen with data science and statistical rigor. This role owns core business analytics while applying advanced methods to evaluate trends, validate hypotheses, and guide strategic decisions. As a senior analytics partner to the ELT and cross-functional partners, this individual translates complex statistical insights into clear business implications, helping leaders frame analytically testable questions and distinguish meaningful signal from noise. Beyond reporting what is happening, this role assesses why it is happening, the level of statistical confidence behind the findings, and what actions the business should take as a result. This is a high-impact IC role responsible for embedding greater statistical discipline into how decisions are made across the organization.

Requirements

5–7+ 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.
AI Fluency & Analytical Innovation
Demonstrated experience using AI tools to accelerate analysis, improve workflow efficiency, or enhance insight generation.
Ability to critically evaluate AI-assisted outputs for accuracy, bias, and statistical soundness.
Curiosity and experimentation mindset around AI-driven analytics and automation.
Understanding of data privacy and responsible AI practices.

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.
Predictive & Advanced Analytics
Develop models or structured analytical approaches to identify drivers of churn, expansion, product adoption, and revenue growth.
Use forecasting techniques to project trends and scenario outcomes.
Identify leading indicators and risk signals across customer and revenue data.
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.