WPC Apple Cash Data Scientist
Role details
Job location
Tech stack
Job description
Take deep dives in large-scale data to identify key insights that will shape future product strategy
Collaborate with cross-functional teams to identify new growth opportunities, develop data requirements, establish product critical metrics, and evangelize data products
Design, deploy, and evaluate measurement plans and feature launch reporting that help define opportunities for higher adoption, improved business performance, and better customer experience
Conduct hypothesis-driven exploratory analyses, select appropriate ML algorithms, and build complex optimization engines to deliver impactful data solutions
Research new technologies and methods across data science and data engineering to improve the technical capabilities of the team
Communicate insights to senior management by distilling complex analysis and concepts into concise business-focused takeaways","responsibilities":"
Requirements
Advanced degree in Applied Econometrics, Statistics, Data Mining, Machine Learning, Analytics, Mathematics, Operations Research, Industrial Engineering, or related field preferred, 5+ years of Python (e.g., Pandas, Polars) with experience working with relational databases, including SQL, and large-scale distributed systems such as Redshift
Expert in designing and implementing end-to-end descriptive data pipelines and business reports that enable data-driven decisions
Practical experience with and theoretical understanding of ML algorithms for classification, regression, clustering, anomaly detection, and casual inference frameworks
Proven ability to extract meaningful business insights, identify root causes behind trends, and recommend actionable strategies
Exceptional data visualization and storytelling abilities, capable of translating complex analyses into clear, executive-ready insights
Excellent written and verbal communication skills, adept at translating technical results into clear, compelling narratives for non-technical and executive audiences
Highly organized, self-driven, and effective at prioritizing and delivering multiple initiatives under tight timelines
Bachelor's degree in engineering, economics, statistics, computer science, or related quantitative field or related experience