Intuit Inc.
We are seeking a highly motivated and experienced Sr. Data Scientist to join our ICS Data and Analytics Team. In this role, you’ll drive data-driven strategies to optimize Customer Success outcomes for both Mid-Market and SMB customers. If you’re passionate about shaping the future of sales technology through data, we’d love to hear from you!
Responsibilities
Define KPIs and Success Metrics: Establish key business indicators for projects, ensuring alignment with company objectives and clear measures of success. Strategic Recommendations: Provide actionable recommendations using diverse data sets and business knowledge, even when complete data is unavailable, to support strategic decisions. Data Visualizations: Translate complex data into clear, accessible visualizations that help stakeholders understand key insights and make informed decisions. Experimentation and A/B Testing: Design, execute, and analyze A/B tests and other experiments using a hypothesis-driven approach. Provide insights and recommendations based on test outcomes to optimize business strategies. Predictive Analytics and Modeling: Develop predictive models and methodologies to uncover growth opportunities and support long-term business planning. Enable Self-Serve Analytics: Define and implement standardized metrics, reports, and dashboards. Work with Data Engineering to ensure data quality and enhance real-time analytic capabilities. AI/GenAI Integration: Collaborate with AI teams to integrate AI/GenAI solutions into business processes, enhancing efficiency and innovation. Cross-Functional Collaboration: Partner with product, digital and customer support teams to identify opportunities, create data-driven strategies, and influence decision-making. 4+ years of experience working with product analytics, web analytics, customer care analytics, or other customer experience analytics Advanced proficiency in SQL, “big data” technologies (e.g., Redshift, Spark, Hive, BigQuery), and BI tools (e.g., Tableau, Qlik, Dash). Qlik certification is a big plus Deep expertise in experimentation design (A/B/n, bandits, painted-door) and causal inference (Propensity Score, DiD, Synthetic Control) with a strategic understanding of their application Understanding of AI-native architectures and GenAI platforms; able to assess implications for data, testing, and behavior Strong business acumen and the ability to translate business strategy into testable hypotheses and learning agendas Strong data storytelling skills, with a proven ability to rapidly construct impactful visualization, communicate insights and influence leadership Excellent communication and interpersonal skills, with a proven ability to build trust and collaborate seamlessly across technical, business, and cross-functional teams. Comfortable working in a fast-paced environment and have flexibility to shift priorities when needed Bachelor’s degree in Engineering, Data Science, Statistics, Mathematics, Computer Science, Economics or related quantitative field; Master’s Degree preferred Preferred: Strong programming skills in Python or R; experience building ML and GenAI models, including automation and custom implementations Experience solving growth-related problems at financial technology companies serving consumer or SMBs Strong analytical and modeling skills using Python (for its rich suite of statistical and modeling libraries like numpy, pandas, scikit-learn, etc.) Familiarity with version control software (git), and general software development
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Define KPIs and Success Metrics: Establish key business indicators for projects, ensuring alignment with company objectives and clear measures of success. Strategic Recommendations: Provide actionable recommendations using diverse data sets and business knowledge, even when complete data is unavailable, to support strategic decisions. Data Visualizations: Translate complex data into clear, accessible visualizations that help stakeholders understand key insights and make informed decisions. Experimentation and A/B Testing: Design, execute, and analyze A/B tests and other experiments using a hypothesis-driven approach. Provide insights and recommendations based on test outcomes to optimize business strategies. Predictive Analytics and Modeling: Develop predictive models and methodologies to uncover growth opportunities and support long-term business planning. Enable Self-Serve Analytics: Define and implement standardized metrics, reports, and dashboards. Work with Data Engineering to ensure data quality and enhance real-time analytic capabilities. AI/GenAI Integration: Collaborate with AI teams to integrate AI/GenAI solutions into business processes, enhancing efficiency and innovation. Cross-Functional Collaboration: Partner with product, digital and customer support teams to identify opportunities, create data-driven strategies, and influence decision-making. 4+ years of experience working with product analytics, web analytics, customer care analytics, or other customer experience analytics Advanced proficiency in SQL, “big data” technologies (e.g., Redshift, Spark, Hive, BigQuery), and BI tools (e.g., Tableau, Qlik, Dash). Qlik certification is a big plus Deep expertise in experimentation design (A/B/n, bandits, painted-door) and causal inference (Propensity Score, DiD, Synthetic Control) with a strategic understanding of their application Understanding of AI-native architectures and GenAI platforms; able to assess implications for data, testing, and behavior Strong business acumen and the ability to translate business strategy into testable hypotheses and learning agendas Strong data storytelling skills, with a proven ability to rapidly construct impactful visualization, communicate insights and influence leadership Excellent communication and interpersonal skills, with a proven ability to build trust and collaborate seamlessly across technical, business, and cross-functional teams. Comfortable working in a fast-paced environment and have flexibility to shift priorities when needed Bachelor’s degree in Engineering, Data Science, Statistics, Mathematics, Computer Science, Economics or related quantitative field; Master’s Degree preferred Preferred: Strong programming skills in Python or R; experience building ML and GenAI models, including automation and custom implementations Experience solving growth-related problems at financial technology companies serving consumer or SMBs Strong analytical and modeling skills using Python (for its rich suite of statistical and modeling libraries like numpy, pandas, scikit-learn, etc.) Familiarity with version control software (git), and general software development
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