Senior AI Data Scientist
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About the Role
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 and dare
- A workplace culture that fosters creativity, diversity and autonomy
- Our borderless, global framework, which enables seamless collaboration
The role
Please note, we are only accepting applicants from the provinces of Ontario and Québec for this role. For Québec-based candidates, fluency in English is necessary because the position entails collaboration with teams based in the rest of Americas and occasionally in Europe.
Role responsibilities
- Lead complex analytical, statistical, machine learning, and applied AI workstreams across multiple business areas, use cases, or stakeholder groups.
- Define data science approaches that align business questions, modeling opportunities, evaluation methods, and measurable outcomes.
- Translate ambiguous business and stakeholder needs into structured analytical strategies, model designs, hypotheses, feature approaches, validation plans, and actionable recommendations.
- Lead the design and execution of models and analyses across use cases such as segmentation, forecasting, propensity modeling, anomaly detection, experimentation analysis, recommendation-oriented analysis, and business decision support.
- Guide the use of structured, semi-structured, and selected unstructured datasets to derive insights and build business-relevant solutions.
- Own and improve notebook-based development, reproducible workflows, and analytical assets in Databricks and other cloud-based environments.
- Apply machine learning and AI methods to support classification, scoring, summarization, pattern detection, feature generation, and business process improvement use cases.
- Evaluate and apply LLM-enabled or AI-assisted workflows where they strengthen analysis, insight generation, decision support, or analytical productivity, while preserving statistical rigor, reproducibility, and human accountability.
- Establish and reinforce best practices for methodology selection, model evaluation, experimentation design, documentation quality, and reproducibility.
- Synthesize modeling outputs, analytical findings, and applied AI results into clear business implications and recommended next steps.
- Serve as a senior partner to client and internal stakeholders by advising on analytical tradeoffs, model usefulness, evaluation rigor, and solution direction.
- Review major analytical and modeling deliverables for clarity, rigor, quality, consistency, and business usefulness, and help raise standards across engagements
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