Sensor Tower is looking for a Data Analyst to act as the primary quantitative auditor of their proprietary SaaS platform's business estimates, investigating discrepancies between internal forecasts and publicly reported data to impact core product and client messaging.
Requirements
- Strong command of SQL for data extraction and manipulation
- Working proficiency in Python or R for statistical analysis
- Familiarity with version control systems (i.e., Git/GitHub) is required
- Demonstrated mastery of quantitative techniques, statistical analysis, and complex data manipulation
- Proven ability to calculate inferring non-explicit metrics from public financial statements and operational data
- Ability to navigate and comprehend the nuances of regulatory filings (10-K, 10-Q, 8-K)
Responsibilities
- Proactively conduct sophisticated, high-fidelity comparisons between proprietary company estimates and publicly available information to identify material variances.
- Employ advanced quantitative techniques to infer or back-solve for proprietary financial and operational metrics from fragmented publicly disclosed data.
- 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.
- 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.
- 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.
Other
- 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.
- Bachelor's degree in a highly quantitative discipline such as Mathematics, Statistics, Economics, Finance, or similar.
- A demonstrable, deep-seated interest in public equity markets, industry dynamics
- Exceptional self-motivation and the capacity to operate with a high degree of independence.
- An unwavering commitment to data accuracy and analytical precision, capable of producing work that withstands rigorous internal and external scrutiny.
- Ability to distill complex quantitative findings into clear, concise, and actionable insights, both written and verbal, for both technical and non-technical audiences.
