Responsibilities:
• The Data Analyst will leverage data on our users to build dashboards, answer key business questions, and tell compelling stories.
• Conduct in-depth analyses of our data, report on key metrics, and provide recommendations that will have a direct impact in shaping future product development and company initiatives.
• Be constantly curious and continue to pursue in-depth explorations of our datasets.
• Work with Product Managers and Engineers to uncover new business opportunities and better understand the levers of the business.
• Support new product launches, A/B testing, ramp-up recommendations, and root cause investigation.
• Leverage new techniques to investigate our data and incorporate external information to boost our knowledge of this market.
• Communicate complicated data findings in a clear and concise manner to our multi-disciplined teams.
• Collaborate with stakeholders including product, marketing, finance, etc. on initiatives that improve our company-level KPIs and match our business priorities.
• Perform intensive investigation and analysis of data anomalies and determine root causes.
• Ad-hoc analysis to address urgent high-impact analysis for business stakeholders.
• Build and maintain Python-based data pipelines and automation scripts to support recurring reporting and analytical workflows.
• Apply data science and machine learning techniques to solve business problems, generate predictive insights, and identify patterns in large datasets.
Qualifications:
• Bachelor’s or Master’s degree in Statistics, Applied Mathematics, Computer Science, or equivalent experience.
• 3+ years of Business Analysis or Data Analysis experience.
• Must have — Proficiency in SQL and/or other data query languages.
• Strong hands-on experience with Python for data analysis, automation, and building ETL pipelines (pandas, NumPy, SQLAlchemy, etc.).
• Familiarity with Product Analytics — understanding and tracking customer and user behavior using lenses like adoption, churn, cohorts, and funnel analysis.
• Experience with data science and machine learning techniques (regression, classification, clustering, time series, etc.) to derive actionable insights and build predictive models.
• Familiarity with ML frameworks such as scikit-learn, XGBoost, or similar libraries.
• Automate reporting via SQL and Python-based ETL frameworks.
• Use of data mining techniques to extract information from data sets and identify correlations and patterns to help build and improve our products.
• The ideal candidate is an intelligent, sharp, detail-oriented, and highly quantitative individual who loves working with and analyzing data.
• Strong analytical skills with the ability to collect, organize, analyze, and disseminate significant amounts of information with attention to detail and accuracy.
• Excellent communication skills — ability to translate complex analytical findings into clear, actionable insights for non-technical stakeholders.
