Required Qualifications:

• Bachelor’s degree in a quantitative field like Economics, Mathematics, Statistics, or a business discipline such as Marketing or Finance. An advanced degree is a plus.
• 1~2 years of experience in marketing analytics or data science with a pronounced operational emphasis.
• Experience managing large datasets.
• Proficiency in SQL, Python/Pyspark, and familiarity with Airflow, Snowflake, GitHub, among other technologies.
• Demonstrated a history of contributing to operational improvements. Strong attention to detail in an environment that requires extensive multi-tasking and multiple deadlines.
• Solid understanding of statistics, experimental design, hypothesis testing, and structured learning from an operational viewpoint.
• Self-motivated and accountable, with a commitment to operational success.
• Strong communication skills to articulate data-driven operational insights

• Manage the CRM & Personalization initiatives, being on-call to respond to urgent alerting and monitoring needs.
• Design, execute, and evaluate A/B tests for various marketing campaigns to meet operational KPIs.
• Serve as the operational expert behind Saks’ personalization program architecture.
• Collaborate with data science and MLOps team to integrate ML models and algorithms into customer-facing marketing campaigns
• Work with MLops and Data engineers to improve the efficiency of automated marketing campaigns
• Develop and launch new personalization campaigns in alignment with business requirements, ensuring they are scalable and efficiently integrated into existing systems.
• Undertake ad-hoc analytics tasks to provide stakeholders with insights for decision-making.
• Stay abreast of new technologies and methodologies to continually enhance operational processes.

Required Qualifications:

• Bachelor’s degree in a quantitative field like Economics, Mathematics, Statistics, or a business discipline such as Marketing or Finance. An advanced degree is a plus.
• 1~2 years of experience in marketing analytics or data science with a pronounced operational emphasis.
• Experience managing large datasets.
• Proficiency in SQL, Python/Pyspark, and familiarity with Airflow, Snowflake, GitHub, among other technologies.
• Demonstrated a history of contributing to operational improvements. Strong attention to detail in an environment that requires extensive multi-tasking and multiple deadlines.
• Solid understanding of statistics, experimental design, hypothesis testing, and structured learning from an operational viewpoint.
• Self-motivated and accountable, with a commitment to operational success.
• Strong communication skills to articulate data-driven operational insights.