We are seeking an experienced Data Scientist to join our DS&A team. In this role, you’ll leverage advanced statistical modeling, machine learning, and data engineering techniques to drive strategic insights and develop scalable predictive solutions across H&R Block’s products and services. You’ll collaborate cross-functionally with data engineers, business leaders, and software developers to translate complex data into actionable business outcomes.
- Bachelor's or Master's degree in Data Science, Statistics, Mathematics, Computer Science, Economics, Engineering, or a related quantitative field, or equivalent combination of education and experience.
- 2–5 years of experience in data science, analytics, machine learning, or a related quantitative field.
- Strong analytical and problem-solving skills with experience applying statistical methods to business problems.
- Experience building, validating, and interpreting predictive, descriptive, and diagnostic models.
- Proficiency in Python or R for data analysis, modeling, and automation.
- Strong SQL or PySpark skills and experience working with large, complex datasets.
- Experience using data visualization and storytelling tools to communicate insights to business stakeholders.
- Ability to translate ambiguous business questions into analytical approaches and actionable recommendations.
- Experience designing and analyzing experiments, including A/B testing and causal measurement techniques.
- Understanding of machine learning techniques such as regression, classification, clustering, and ensemble methods.
- Ability to perform data exploration, feature engineering, model evaluation, and performance monitoring.
- Familiarity with big data platforms and distributed computing technologies such as PySpark, Databricks, or Spark.
- Experience working in cloud-based analytics environments such as Azure, AWS, or GCP.
- Knowledge of software development best practices, including Git version control and code review processes.
- Experience with customer analytics, retention modeling, segmentation, forecasting, or recommendation systems.
- Familiarity with causal inference, uplift modeling, or advanced experimentation techniques.
- Ability to influence decisions through clear communication and data-driven storytelling.
- Experience working with product, marketing, financial services, retail, or digital customer data.
- Familiarity with funnel analysis, customer segmentation, and year-over-year performance tracking.
- Experience in tax, financial services, retail, or other consumer-focused industries is preferred.
- Experience mentoring junior analysts or data scientists.
- Exposure to generative AI, large language models (LLMs), or applied AI solutions is a plus.