Senior Data Scientist

Why Valtech? We’re the experience innovation company - a trusted partner to the world’s most recognized brands. To our people we offer growth opportunities, a values -driven culture, international careers and the chance to shape the future of experience.

The opportunity

At Valtech, you’ll find an environment designed for continuous learning, meaningful impact, and professional growth. Whether you're pioneering new digital solutions, challenging conventional thinking or building the next generation of customer experiences, your work will help transform industries.

We are proud of:

The work we do and the innovation we drive

Our values of share, care a nd dare

A workplace culture that fosters creativity, diversity and autonomy

Our borderless, global framework, which enables seamless collaboration

The role

We are looking for an experienced Senior Data Scientist to drive advanced analytics, causal reasoning, and AI-powered decision intelligence across multiple use cases within our AI portfolio. This role goes beyond traditional modeling and focuses on building production-grade systems that combine predictive, causal, and generative AI capabilities to directly influence business outcomes.

You will work at the intersection of data science, machine learning, and GenAI, turning complex data into actionable insights, automated decisions, and intelligent workflows across domains such as marketing, operations, forecasting, and optimization.

This is not a purely retrospective analytics role. You will design and deploy systems that integrate experimentation, observational data, machine learning, and generative AI into real-time or near-real-time decision-making pipelines.

You will collaborate closely with data engineers, ML engineers, analysts, and platform teams, contributing to shared modeling standards and cross-functional AI architecture.

Role responsibilities

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

Must have qualifications

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 qualifications

Deep experience in marketing analytics, a…