Data Science Manager – Marketing & Strategy

Job description

To support its growth, Sia Partners is recruiting a Data Science Manager with strong expertise in marketing, client, and product challenges. You will join the global Data Science capability at Sia Partners, consisting of 300+ Data Scientists located across Europe, North America, and Asia. You will be part of the growing US Data Science team, focusing on marketing analytics for various sectors including luxury, energy, FMCG, banking, and insurance.

Key Responsibilities

• Leading Data Science Projects: Manage the delivery of data science projects, working across several clients in the US.
• Application of Data Science in Marketing: Apply data science techniques to complex marketing problems including customer segmentation, predictive modeling, pricing strategy, customer experience enhancement, and more.
• Business Development: Collaborate with Sia Partner’s US Data Science Lead, Business Units teams, and Data Science centers from other locations to develop proposals for potential opportunities.
• Client Engagement: Identify opportunities to expand Sia Partners' Data Science offering to existing clients.
• Professional Networking: Build and maintain external professional peer networks to drive new revenue opportunities.
• Marketing and Thought Leadership: Support marketing campaigns, contribute to external positioning across social networks, work on blogs, and develop Sia Partners’ assets, including methodologies and tools.
• Recruitment and Team Development: Lead recruitment initiatives for the Data Science team, advise on market positioning, and support the operational and career management of data science consultants.
• Coaching and Mentoring: Mentor colleagues in performance and development, facilitate learning, and enhance the team's technical knowledge and consulting skills.

Some examples of use cases this person would tackle:

- CRM analytics and predictive modelling:

• Deepen understanding of customers through segmentation and clustering techniques for optimal marketing campaign personalization.
• Develop and implement predictive models to anticipate customer behaviors, optimize marketing campaigns, and support customer retention strategies.
• Define hyper-personalized marketing plans.

- Customer experience data mining and personalization models:

• Analyze customer feedback using NLP models to extract structured information and identify causes of dissatisfaction.
• Leverage AI models to offer hyper-personalized customer experiences, including personalized products, recommendation engines, and connected objects.

- Pricing & Product Analytics:

• Contribute to pricing strategy, product development, and assortment optimization.
• Enhance cross-selling strategies and capitalize on Generative AI for innovative product creation.

- Open Data:

• Integrate open data and geomarketing data into analyses to optimize targeting and campaign impacts.
• Understand and anticipate the performance of the retail network.

- Merchandising & Operations analytics and optimization:

• Predict sales to adjust production and inventory.
• Model the optimization of the entire distribution chain.

- Internal Activities:

• Develop and enhance offerings through training, working groups, and R&D work.
• Contribute to the firm's visibility through publications, sector blogs, studies, press releases, and marketing events.
• Support business development by defining needs and participating in commercial activities.