Senior Data Scientist

This role is for one of the Weekday's clients

Senior Data Scientist

campaign management, loyalty programs, and customer behavior insights

data-driven marketing

post-campaign analysis

assortment planning

A/B testing

Requirements

Key Responsibilities

• Campaign Analytics & Management: Design, execute, and analyze marketing campaigns across various channels (email, digital, in-app, etc.). Use statistical models and machine learning techniques to forecast outcomes and optimize targeting.
• Loyalty Program Analytics: Analyze the performance of customer loyalty initiatives, understand drivers of customer retention, churn, and engagement, and recommend enhancements to improve program ROI and customer LTV.
• A/B Testing & Experimentation: Design and manage controlled experiments to test new campaign strategies, product changes, or personalization tactics. Ensure robust experimental design and draw actionable insights from results.
• Post-Campaign Analysis: Conduct deep-dive analyses after campaign execution to measure impact, identify learnings, and refine future marketing efforts.
• Customer Behavior & Segmentation: Analyze transactional, behavioral, and demographic data to uncover customer insights. Build customer segments and personas that can be leveraged for targeting and personalization.
• Assortment Planning Support: Provide data-driven recommendations to optimize product assortment and inventory planning based on customer preferences, purchase behavior, and historical performance.
• Stakeholder Collaboration: Work closely with marketing, CRM, product, and analytics teams to translate business questions into data-driven solutions. Present findings in a clear, concise manner to both technical and non-technical stakeholders.
  

Requirements

• 6-10 years of experience in data science, with a strong focus on marketing analytics and campaign performance.
• Proven experience working with loyalty programs, A/B testing frameworks, and post-campaign impact analysis.
• Proficient in SQL, Python/R, and data visualization tools like Tableau, Power BI, or Looker.
• Experience with predictive modeling, segmentation, and customer lifetime value (CLV) analytics.
• Strong knowledge of assortment planning concepts and how customer preferences influence merchandising decisions.
• Excellent problem-solving, communication, and stakeholder management skills.
• Prior experience in retail, e-commerce, or consumer-focused industries is a strong plus.
• Bachelor's/Master's degree in Statistics, Data Science, Computer Science, Economics, or a related field