Senior Data Scientist
Edmond, OKFull-TimeSeniorAI / Data Science
What You’ll Do
- Analyze large-scale datasets in BigQuery to extract actionable insights on user behavior, engagement, and app performance.
- Develop metrics and KPIs to track user acquisition, retention, and in-app behaviors across multiple apps.
- Conduct funnel analysis, cohort analysis, and segmentation studies to understand user journeys and identify growth opportunities.
- Design, build, and deploy predictive models to enhance app personalization, recommend content, and improve ad targeting.
- Develop models for churn prediction, lifetime value estimation, and user segmentation to inform product and marketing strategies.
- Leverage BigQuery ML and other cloud-based machine learning tools to streamline the modeling process within the Google ecosystem.
- Design and analyze A/B tests to evaluate the impact of new features, UX changes, and marketing strategies on user engagement and retention.
- Implement statistical methods to assess test results, including sample size calculation, significance testing, and post-test analysis.
- Document and share best practices for experimentation with cross-functional teams.
- Work closely with data engineers to build, optimize, and maintain ETL pipelines in BigQuery, ensuring data availability and accuracy.
- Define and implement data quality checks and standardization procedures for mobile app data.
- Advocate for data infrastructure improvements and guide the design of efficient data pipelines.
- Develop interactive dashboards and visualizations in tools such as Tableau, or Looker to present insights to product, marketing, and engineering teams.
- Translate complex data insights into clear, actionable recommendations for both technical and non-technical stakeholders.
- Present findings regularly to senior leadership, providing strategic guidance based on data-driven insights.
- Mentor junior data scientists and analysts, providing guidance on best practices in data science, mobile app analytics, and BigQuery usage.
- Lead cross-functional data projects, ensuring alignment on goals, methodology, and timelines.
- Stay updated with industry trends and new technologies, sharing relevant knowledge with the team and encouraging innovation.
Skills Needed to Succeed
- Ability to self-motivate, make independent decisions, and solve problems with innovation.
- Effective at multi-tasking and time management to meet strict deadlines while remaining flexible and open to change.
- Excellent verbal, written, and interpersonal communication skills to clearly explain complicated processes and foster partnerships.
- Effective at process and organizational management to coordinate, structure, and provide vision to projects.
- Strong leadership skills and understanding of developing and guiding others.
- A strong background in data science, experience with mobile app analytics, and expertise in leveraging Google BigQuery for large-scale data processing and analysis.
- Proficiency in Python or R for data analysis, modeling, and machine learning.
- Familiarity with data visualization tools like Google Data Studio, Tableau, or Looker to create dashboards and reports.
- Strong understanding of mobile app analytics, including metrics for user engagement, retention, and funnel analysis preferred.
- Experience with app tracking frameworks and attribution tools(e.g., Firebase, Adjust, AppsFlyer) preferred.
- Knowledge of statistical techniques for experiment design and analysis preferred(e.g., causal inference, propensity score matching).
- Familiarity with app store optimization(ASO) and mobile marketing analytics preferred.
- Master’s degree or Ph. D. in Data Science, Statistics, Computer Science, Applied Mathematics, or a related field.
- 5+ years of experience in data science or analytics, with at least 2 years in the mobile app industry.
- Advanced proficiency in Google BigQuery, including SQL query optimization, data processing, and BigQuery ML for machine learning.
- Experience with machine learning algorithms for classification, regression, clustering, and time series analysis.
