Data Science Manager – Marketing & Strategy

Company Description

About Sia 

Sia is a next-generation, global management consulting group. Founded in 1999, we were born digital. Today our strategy and management capabilities are augmented by data science, enhanced by creativity and driven by responsibility. We’re optimists for change and we help clients initiate, navigate and benefit from transformation. We believe optimism is a force multiplier, helping clients to mitigate downside and maximize opportunity. With expertise across a broad range of sectors and services, our 3,000 consultants serve clients worldwide from 48 locations in 19 countries. Our expertise delivers results. Our optimism transforms outcomes. 

Sia’s AI & Data Business Unit is the powerhouse of our firm’s innovation—merging cutting-edge Data Science, Generative AI, and advanced digital solutions to transform industries. With over 350 experts worldwide, we tackle projects from proof-of-concept to large-scale deployment, always pushing the boundaries of AI capabilities. Our 12 R&D labs in Europe and North America drive continuous research in areas like computer vision, MLOps, and deep learning, partnering closely with our business consultants for real-world impact. By joining Sia’s AI & Data team, you’ll step into a vibrant, collaborative environment that nurtures professional growth and empowers you to shape the future of AI-driven consulting. 

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 visi