Senior Data Scientist

Valtech is an experience innovation company that blends crafts, categories, and cultures to help brands unlock new value in a digital world. They drive transformation for leading organizations including L’Oréal, Mars, Audi, P&G, Volkswagen, and Dolby. The company focuses on data, AI, creativity, and technology to deliver impactful solutions.

Data Science & Statistical Expertise:

• Strong experience in machine learning, statistics, and applied data science
• Experience with causal inference, experimentation, or decision science methodologies
• Solid understanding of forecasting, optimization, or analytical modeling techniques

Technical Skills:

• Strong programming skills in Python and SQL
• Experience building and deploying production-ready data science or ML systems
• Familiarity with model lifecycle management (training, deployment, monitoring)

Cloud & Platform Experience (Key Requirement):

• Hands-on experience with at least one major cloud platform: Azure (preferred), AWS, or GCP
• Experience working with modern data and AI platforms (e.g., Azure ML / Azure AI, Databricks, or similar ecosystems)

Domain & Data Experience:

• Experience working with complex, multi-source datasets (e.g., transactional, behavioral, operational data)
• Ability to translate business problems into analytical frameworks

Mindset:

• Strong problem-solving skills with focus on business impact
• Ability to translate complex models into actionable decisions
• Strong collaboration and communication skills across technical and business teams

Nice to have:

• Deep experience in marketing analytics, attribution, or campaign measurement
• Hands-on experience with uplift modeling, geo experiments, synthetic control, Marketing Mix Modeling (MMM)
• Experience with GenAI frameworks (e.g., LangChain, LangGraph, RAG architectures, agent frameworks)
• Familiarity with data engineering tools (e.g., Spark, Airflow, dbt)
• Experience with platforms such as Snowflake, Fabric, or BigQuery
• Exposure to advanced time-series methods and probabilistic forecasting
• Experience working in Agile, product-led, or consulting environments

Advanced Analytics & Machine Learning:

• Develop and deploy machine learning models across use cases (forecasting, optimization, recommendation systems)
• Apply statistical, predictive, and prescriptive modeling techniques to solve business problems
• Build reusable modeling frameworks that can scale across multiple domains

Causal Inference & Decision Intelligence:

• Design and implement causal inference methods (e.g., uplift modeling, experiments, quasi-experimental methods)
• Translate observational and experimental data into actionable business insights
• Embed causal reasoning into decision systems that guide actions (e.g., optimization, prioritization, trade-offs)

Generative AI & Intelligent Systems:

• Integrate GenAI capabilities (e.g., LLMs, RAG pipelines, agent-based systems) into data science workflows
• Contribute to the development of intelligent agents and AI-assisted decision-making systems
• Combine structured data models with unstructured data and GenAI outputs

Forecasting & Optimization:

• Build forecasting models (time-series, probabilistic, causal) to support planning and operations
• Develop optimization approaches for resource allocation, scheduling, or campaign performance
• Ensure models are explainable and actionable in business contexts

Production & Platform Integration:

• Build and maintain production-grade data science solutions
• Collaborate with engineering teams to integrate models into scalable APIs and platforms
• Ensure robustness, monitoring, and lifecycle management of deployed models

Cross-Functional Collaboration:

• Partner with data engineering, analytics, and product teams to ensure data readiness and solution adoption
• Review and validate modeling approaches across teams (forecasting, experimentation, ML)
• Contribute to best practices in AI, ML, and data science within the organization

This is a position based in Poland.