Valtech - Senior Data Scientist
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Requirements
• 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 • Strong programming skills in Python and SQL • 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) • 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 • Deep experience in marketing analytics, attribution, or campaign measurement • 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) • GenAI frameworks • Familiarity with data engineering tools (e.g., Spark, Airflow, dbt) • data engineering tools • Experience with platforms such as Snowflake, Fabric, or BigQuery • Snowflake, Fabric, or BigQuery • Exposure to advanced time-series methods and probabilistic forecasting • time-series methods and probabilistic forecasting • Experience working in Agile, product-led, or consulting environments • Agile, product-led, or consulting environments • Commitment to reaching all kinds of people • We design experiences that work for all kinds of people - and that starts with our own teams. At Valtech, we’re intentional about building an inclusive culture where everyone feels supported to grow, thrive and achieve their goals. No matter your background, you belong here. Explore our Diversity & Inclusion site to see how we’re creating a more equitable Valtech for all.
Responsibilities
• Advanced Analytics & Machine Learning • Develop and deploy machine learning models across use cases (forecasting, optimization, recommendation systems) • 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
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