Data Scientist

Our client, a leader in tax software with amazing culture, is hiring for a contract Data Scientist III. This is a remote position with PST hours.

This role will focused on customer growth and customer segmentation (acquisition, retention), building segmentation models and strategies for product and marketing teams to inform roadmapping and planning. This role will manage/build data pipelines and dashboards so strong skills in SQL, Python and Tableau or Qlik Sense are huge.

Contract Duration: 11 Months, Potential to Extend

Required Skills & Experience

• Strong SQL skills for complex scripting + experience with data wrangling tools.
• Proven experience in data visualization with Tableau or Qlik Sense.
• Solid Python skills for for advanced analytics.
• Demonstrated experience applying descriptive and predictive analytics to real-world business problems.
• Familiarity with leveraging generative AI tools (e.g., ChatGPT) to expedite SQL/R code generation and streamline the creation of analytical reports and visualizations is a huge plus.

What You Will Be Doing

Daily Responsibilities

• Design and Maintain E2E Data Pipelines: Develop and manage scalable end-to-end data pipelines, ensuring data accuracy and availability across customer acquisition and retention workflows.
• Ensure Data Quality and Governance: Proactively monitor and resolve data integrity issues; implement standards and documentation to ensure reliability and transparency in analytics processes.
• Build Actionable Dashboards and Reporting Tools: Create and maintain intuitive dashboards (via Tableau or Qliksense) and automated reports that track key performance indicators, customer segments, and other customer lifecycle metrics.
• Optimize Customer Funnels and Journeys: Analyze key drop-off points in acquisition and retention funnels; identify and recommend opportunities to improve customer experiences and boost conversion across touchpoints.
• Generate Descriptive and Predictive Customer Insights: Analyze customer behavior and lifecycle trends using statistical and machine learning methods to uncover actionable insights and predict outcomes such as churn, conversion, and LTV.
• Translate Insights into Business Recommendations: Synthesize analytical findings into compelling narratives and strategic recommendations that inform marketing and product decisions during tax season.