Head of Data Science & Analytics (Retail)

Salary: ฿150,000 – ฿250,000

About the Role
We are seeking a highly motivated and experienced Head of Data Science & Analytics to lead our data-driven initiatives and drive significant business growth. In this critical role, you will be responsible for developing and executing a cutting-edge analytics strategy that leverages data to optimize every aspect of our retail operations, from customer acquisition and engagement to inventory management and pricing. You will lead and mentor a talented team of data scientists and analysts, fostering a culture of innovation and excellence.

Key Responsibilities:

Leadership & Strategy:

• Develop and execute a forward-thinking analytics strategy tailored to the retail industry, focusing on leveraging data platforms to drive revenue growth, operational efficiency, and customer satisfaction.
• Lead, mentor, and inspire a team of data scientists and analysts, fostering a culture of innovation, collaboration, and data-driven decision-making.
• Stay ahead of industry trends, emerging technologies, and best practices in data science and retail analytics to maintain our competitive edge.

Analytics Execution:

• Oversee the integration of diverse data sources, including POS systems, CRM platforms, online transactions, and third-party providers, into our cloud-based data platform.
• Design and develop advanced machine learning models, algorithms, and statistical analyses to uncover actionable insights related to customer behavior, product performance, and market trends.
• Apply expertise in recommendation and personalization algorithms to enhance customer experiences and engagement.
• Deliver data-driven solutions to optimize pricing strategies, inventory management, and promotional campaigns, leveraging state-of-the-art analytics tools and methodologies.

Business Partnership:

• Partner closely with retail operations, marketing, and sales teams to understand business challenges and provide tailored analytical support that aligns with strategic objectives.
• Identify opportunities to enhance customer segmentation, personalized marketing efforts, and customer retention strategies through advanced data science techniques.
• Act as a key advisor to senior leadership, translating complex data insights into actionable recommendations and business value.

Performance Monitoring & Optimization:

• Define and monitor key performance indicators (KPIs) related to retail operations, such as sales conversion rates, customer lifetime value, and basket analysis.
• Leverage analytics to continuously assess and optimize business processes, driving operational efficiency and profitability.

Communication & Presentation:

Present complex analytical findings, models, and recommendations to stakeholders in a clear, impactful, and visually compelling manner.
Collaborate across departments to implement data-driven initiatives that align with our goals and drive tangible outcomes.

Qualifications:

Education & Experience:

• Bachelor’s degree in Statistics, Mathematics, Computer Science, Data Science, Economics, or a related field (Master’s or PhD strongly preferred).
• Extensive experience in analytics, data science, or business intelligence roles, with significant exposure to the retail industry.

Technical Skills:

• Advanced proficiency in Python, R, SQL, and machine learning frameworks.
• Expertise in data visualization tools (e.g., Tableau, Power BI) and cloud-based data platforms (e.g., AWS, GCP, Azure).
• In-depth knowledge of big data technologies (e.g., Spark, Hadoop) and modern data engineering practices.
• Strong understanding of recommendation/personalization algorithms and data processing technologies.

Leadership & Business Acumen:

• Proven ability to lead high-performing teams in a dynamic, fast-paced environment.
• Exceptional strategic thinking and problem-solving skills with a demonstrated focus on delivering business value.
• Deep understanding of retail operations, including inventory management, customer journey mapping, and merchandising strategies.