Sensor Tower
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Data Analyst
role at
Sensor Tower
We 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, including 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, demanding 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, and 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
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.
Demonstrated mastery of quantitative techniques, statistical analysis, and complex data manipulation, with proven ability to calculate and infer non‑explicit metrics from public financial statements and operational data.
Deep 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).
Strong command of SQL for data extraction and manipulation; working proficiency in Python or R for statistical analysis; familiarity with version control systems (Git/GitHub) is required.
Exceptional self‑motivation and the capacity to operate with a high degree of independence; proactive in identifying analytical opportunities rather than merely executing directed tasks.
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 for both technical and non‑technical audiences.
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Data Analyst
role at
Sensor Tower
We 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, including 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, demanding 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, and 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
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.
Demonstrated mastery of quantitative techniques, statistical analysis, and complex data manipulation, with proven ability to calculate and infer non‑explicit metrics from public financial statements and operational data.
Deep 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).
Strong command of SQL for data extraction and manipulation; working proficiency in Python or R for statistical analysis; familiarity with version control systems (Git/GitHub) is required.
Exceptional self‑motivation and the capacity to operate with a high degree of independence; proactive in identifying analytical opportunities rather than merely executing directed tasks.
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 for both technical and non‑technical audiences.
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