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. 

To support its growth, Sia is recruiting a Data Scientist Consultant with a strong interest in Marketing, Customer, and Product topics and challenges. 

Reporting to Sia's Data Science teams, your role is to support our clients' operational teams and marketing departments in defining their data needs, analyzing their data, deploying data science algorithms, and AI agents and providing strategic decision support. 

These engagements require the implementation of complex analyses to support our clients, notably in: 

• Forward-Deployed Engineering 
• Generative AI, Machine Learning & Statistics 
• Data Analysis for strategic support 
• Data Engineering & DevOps 
• Data Transformation & Acculturation 

A few examples of engagements: 

• Automatic detection and classification of user needs: Identifying key themes and specific needs from free-text exchanges across various sources (surveys, email exchanges, etc.) using generative AI, NLP processing, and clustering. 

• Sales forecasting to optimize new product assortment in stores: Developing a machine learning model to estimate the sales potential of new luxury products in each store across the network, in order to provide product assortment recommendations for stores. 

• Building and scaling audience segmentation for data-driven marketing at scale: Designing and industrializing audience signals and segmentation capabilities within customer data platforms to power data-driven marketing campaigns reaching millions of customers, enabling more precise targeting and measurable campaign performance. 

Key Responsibilities:  

• Partner with our clients' leadership teams, engineers, program managers, and data analysts to understand data needs. 
• Identify, acquire, process, and explore relevant data sources. 
• Analyze large amounts of structured and unstructured information to discover trends and patterns. 
• Use your data analytics and data science expertise to derive valuable insights from datasets, and build predictive models and machine learning algorithms. 
• Design, build, fine-tune, and deploy LLM-based systems using models such as GPT, Claude, Gemini, Llama, and Mistral. 
• Develop GenAI-powered applications (self-managed and API-based) that meet real business needs and comply with applicable regulations (GDPR, EU AI Act, model licenses). 
• Design and optimize prompting strategies (few-shot, Chain/Tree-of-Thought, ReAct, self-reflection, guardrails) to improve reliability and control. 
• Communicate at scale through multiple mediums: presentations, dashboards, datasets, bots, and more. 
• Propose solutions and strategies to business challenges. 
• Collaborate with engineering and product development teams. 
• Work across a broad range of partners and projects, delivering models, algorithms, datasets, measurements, services, tools, and processes. 

• Master's degree or Engineering degree (or equivalent) in Computer Science, Engineering, Data Science, or a related quantitative field.