Senior AI Data Scientist

Valtech

3h•CA$70,000 - CA$120,000•Remote

About The Position

The Senior AI Data Scientist is a senior individual contributor role within Data Science, AI, & Agentic. This role is responsible for leading complex analytical, predictive, and applied AI workstreams, operating with a high degree of independence and serving as a trusted advisor on data science and AI strategy. This role focuses on shaping and executing advanced modeling, experimentation, and applied AI solutions that address high-value business problems. The Senior AI Data Scientist translates ambiguous stakeholder needs into structured analytical approaches, model designs, evaluation frameworks, and actionable insights. They partner closely with cross-functional teams and stakeholders to ensure that analytical and AI-driven solutions are both technically sound and practically impactful. At this level, the role combines strong quantitative expertise with deep modeling capability, rigorous experimentation practices, and thoughtful application of AI methods. The Senior AI Data Scientist influences analytical direction, improves delivery quality through best practices and reusable approaches, and elevates the overall effectiveness of data science workstreams across engagements.

Requirements

Deep working knowledge of statistics, probability, machine learning, experimentation, and analytical problem solving.
Strong ability to define data science approaches and modeling strategies in complex business environments.
Strong people leadership skills, including coaching, feedback, prioritization, and support for team development.
Strong understanding of supervised and unsupervised learning, feature engineering, model evaluation, experimentation design, and error analysis.
Strong ability to work with structured, semi-structured, and selected unstructured datasets.
Strong familiarity with applied AI methods, including LLM-enabled workflows, text-oriented analysis, AI-assisted feature extraction, summarization, and classification.
Strong familiarity with notebook-based development and collaborative data science workflows, including Databricks and MLflow-supported experimentation.
Ability to evaluate where applied AI strengthens a use case and where classical statistical or machine learning methods are more appropriate.
Strong stakeholder management skills and the ability to communicate clearly with technical and non-technical audiences.
Ability to balance delivery quality, team workload, business urgency, and stakeholder expectations across multiple workstreams.
Strong written and verbal communication skills in English, including confidence in client-facing and leadership-facing settings.
Ability to collaborate effectively across distributed teams in the Americas and across multiple disciplines.
Expected to be an active adopter of approved AI-enabled analytical, coding, experimentation, documentation, and productivity workflows that improve the quality and speed of data science work.
Uses AI-assisted workflows to support exploratory analysis, feature thinking, code and notebook development, model documentation, experiment design, analytical summarization, and stakeholder communication while maintaining human accountability for method selection, statistical reasoning, validation, interpretation, and final recommendations.
Understands that AI-generated code, modeling suggestions, analytical summaries, or methodological recommendations must be reviewed against source data, assumptions, statistical rigor, business context, governance expectations, and reproducibility standards before use.
Demonstrates curiosity and practical enthusiasm for applying AI to improve analytical leverage, decision support, and delivery quality without weakening scientific discipline or human judgment.
AI fluency means using AI responsibly while helping a team adopt AI-enabled practices with consistency and care.
Expected to coach practitioners on safe, useful, and role-appropriate AI adoption; review AI-assisted outputs for quality and governance; and improve team delivery habits without weakening accountability or craft standards.
Python
Jupyter Notebooks
Pandas
NumPy
scikit-learn
SciPy
Statsmodels
XGBoost
LightGBM
Databricks
Databricks notebooks
Databricks Machine Learning
Apache Spark
PySpark
MLflow
SQL
BigQuery
Snowflake
Google Cloud Platform (GCP)
Vertex AI
Microsoft Azure
Azure AI services
Azure Machine Learning
OpenAI-compatible APIs or enterprise LLM platforms as relevant to the client environment
Prompt evaluation and structured testing workflows
Embedding, text analysis, and unstructured data processing patterns
Model and workflow evaluation tooling as relevant to the client environment
Matplotlib
Seaborn
Plotly
Looker
Power BI
Tableau
Git
GitHub
Azure DevOps

Nice To Haves

Databricks associate or professional-level training or certification
Google Cloud data, ML, or AI training
Microsoft Azure data, ML, or AI training
Python, machine learning, experimentation, or applied AI coursework
Statistics, forecasting, or analytical modeling training
Leadership, coaching, or people management training is a plus

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 through reusable patterns and stronger delivery practices.
Collaborate with AI Scientists, AI Engineers, Analytics Engineers, Data Engineers, and Architects to align solutions with business needs, data realities, platform constraints, and technical patterns.
Mentor junior and mid-level practitioners through technical guidance, quality review, and best-practice sharing, without formal people-management responsibility.
Improve data science delivery by identifying opportunities for better workflows, stronger evaluation, clearer documentation, more scalable notebooks, and reusable analytical assets.
Follow established governance, privacy, and responsible data and AI use standards in day-to-day work.

Benefits

Comprehensive insurance plan (Gold, Silver, or Bronze options with employer contribution up to 80%)
Short- and long-term disability coverage
Virtual healthcare services via Dialogue
Employee and Family Assistance Program
Complete mental health support program
$500 Personal Spending Account (for healthcare reimbursements, gym memberships, public transit passes, office supplies, or RRSP contributions)
Retirement plan with 100% RRSP matching by Valtech up to 4% through a Deferred Profit Sharing Plan (DPSP)
Flexible vacation policy
Personal Technology Reimbursement – $30/month
Winter holiday closures
Flexible scheduling