We are seeking a talented and experienced Data Scientist to join our team and drive improvements in delivery promise times, generate personalised recommendations, and implement custom segmentation strategies. As a Data Scientist in our organisation, you will play a pivotal role in leveraging data-driven insights to enhance our overall customer experience and operational efficiency.
Responsibilities: Combine historical purchasing trends with forward-looking signals to help the business procure just the right amount of products at the right time to the right locations. Analyse historical delivery data to identify patterns, trends, and potential areas for improvement in delivery promise times. Develop predictive models to accurately estimate delivery times based on various factors such as customer location, rider availability, and historical performance. Continuously monitor and evaluate the performance of delivery promise models, making adjustments as necessary to optimise accuracy. Design and implement recommendation engines for personalised product suggestions and content. Collaborate with product teams to understand user behaviour, preferences, and feedback, incorporating these insights into Zapp’s recommendations. Experiment with different recommendation algorithms, A/B testing methodologies, and optimization strategies to improve the relevance and effectiveness of recommendations. Work closely with engineering teams to integrate recommendation models into our platforms and ensure seamless user experiences. Develop and implement custom segmentation strategies based on user demographics, behaviour, and transaction history. Collaborate with marketing teams to identify target segments for personalised campaigns and promotions. Provide actionable insights and recommendations to stakeholders based on the analysis of segmented data. Preferred Skills: 3-5 years of experience in e-commerce, logistics, or a related industry. Familiarity with big data technologies (e.g., Hadoop, Spark) and cloud platforms (e.g., AWS, Azure, GCP). Knowledge of database systems and SQL. Have built and maintained recommendation models in production. Department:
Engineering Locations:
HQ
#J-18808-Ljbffr
Responsibilities: Combine historical purchasing trends with forward-looking signals to help the business procure just the right amount of products at the right time to the right locations. Analyse historical delivery data to identify patterns, trends, and potential areas for improvement in delivery promise times. Develop predictive models to accurately estimate delivery times based on various factors such as customer location, rider availability, and historical performance. Continuously monitor and evaluate the performance of delivery promise models, making adjustments as necessary to optimise accuracy. Design and implement recommendation engines for personalised product suggestions and content. Collaborate with product teams to understand user behaviour, preferences, and feedback, incorporating these insights into Zapp’s recommendations. Experiment with different recommendation algorithms, A/B testing methodologies, and optimization strategies to improve the relevance and effectiveness of recommendations. Work closely with engineering teams to integrate recommendation models into our platforms and ensure seamless user experiences. Develop and implement custom segmentation strategies based on user demographics, behaviour, and transaction history. Collaborate with marketing teams to identify target segments for personalised campaigns and promotions. Provide actionable insights and recommendations to stakeholders based on the analysis of segmented data. Preferred Skills: 3-5 years of experience in e-commerce, logistics, or a related industry. Familiarity with big data technologies (e.g., Hadoop, Spark) and cloud platforms (e.g., AWS, Azure, GCP). Knowledge of database systems and SQL. Have built and maintained recommendation models in production. Department:
Engineering Locations:
HQ
#J-18808-Ljbffr
