United States onlyWe are seeking a highly motivated and quantitatively adept Data Analyst to join our Sensor Tower team. This pivotal, highly visible role is designed for an emerging professional with 3-5 years of experience and who possesses a robust interest in public markets, business dynamics, and sophisticated financial analysis.
The core function of this role is to act as the primary quantitative auditor of our proprietary SaaS platform's business estimates. You will proactively and autonomously investigate discrepancies between our internal forecasts and publicly reported data (e.g., active users, revenue, growth metrics from public company filings). This is not a standard reporting role; it requires a strong intellectual curiosity, an ability to back into complex, non-obvious metrics using publicly available documentation (10-Ks, 10-Qs, Investor Presentations), and the ability to articulate findings that directly impact our core product and client messaging. The ideal candidate will thrive in an ambiguous environment and possess the self-starter mentality often developed in roles like sell-side research, entry-level strategy consulting, or similar quantitative roles.
Base salary: $100,000 - $120,000
What you will do:
- Quantitative Audit and Discrepancy Analysis: Proactively conduct sophisticated, high-fidelity comparisons between proprietary company estimates and publicly available information to identify material variances. This includes rigorously researching differences in underlying definitions, methodological frameworks, and reporting methodologies.
- Reconciling and Reverse-Engineering Estimates: Employ advanced quantitative techniques to infer or back-solve for proprietary financial and operational metrics from fragmented publicly disclosed data. This demands a nuanced understanding of regulatory filings (e.g., extracting non-GAAP to GAAP reconciliations or segment reporting details).
- Documentation and Knowledge Repository Contribution: Create clear and concise analytical documentation, including a full trace of the quantitative proof. Maintain and contribute this analysis, complete with reproducible data trails, to the team's shared GitHub repository.
- Cross-Functional Impact Assessment: Collaborate closely with the Data Science and Engineering teams to model the collateral impact of a fundamental estimate change across the firm's broader suite of financial models and forecasts.
- Stakeholder Communication and Enablement: Support the Data Science, Sales, and Client Success teams by developing clear, compelling, and accurate messaging that articulates the rationale behind our estimate methodology, the nature of identified discrepancies, and the impact of changes.
Required Experience and Skills
- Experience: 3 to 5 years of professional experience in a highly analytical, quantitatively driven role, with a strong preference for backgrounds in Sell-Side Research, Equity Analysis, Management Consulting, or Similar quantitative roles.
- Academic Rigor: Bachelor's degree in a highly quantitative discipline such as Mathematics, Statistics, Economics, Finance, or similar.
- Advanced Quantitative Acumen: Demonstrated mastery of quantitative techniques, statistical analysis, and complex data manipulation. Must possess a proven ability to calculate inferring non-explicit metrics from public financial statements and operational data.
- Business and Market Intuition: A demonstrable, deep-seated interest in public equity markets, industry dynamics, and an ability to navigate and comprehend the nuances of regulatory filings (10-K, 10-Q, 8-K).
- Technical Proficiency: Strong command of SQL for data extraction and manipulation, and working proficiency in Python or R for statistical analysis. Familiarity with version control systems (i.e., Git/GitHub) is required.
- Intellectual Autonomy/ Self-Starter: Exceptional self-motivation and the capacity to operate with a high degree of independence. Must be proactive in identifying analytical opportunities rather than simply executing directed tasks.
- Precision and Attention to Detail: An unwavering commitment to data accuracy and analytical precision, capable of producing work that withstands rigorous internal and external scrutiny.
- Executive Communication: Ability to distill complex quantitative findings into clear, concise, and actionable insights, both written and verbal, for both technical and non-technical audiences.
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