Data Scientist

Job summary

Partner with client businesses to develop analytical strategies, build models, define performance metrics, and measure outcomes through experiments and attribution. Analyze customer and marketing data, advise on audience strategy, and present actionable findings to senior stakeholders.

Job details

Role : Data Scientist Location: 2-day(Toronto) Job type: Contract(6 Months) - High possibility of Extension Pay : CAD $85 /Hour Start date : Immediate Role Overview The Senior Analytics & Insights Consultant sits within Adastra's AI practice and serves as an embedded, end-to-end analytics partner to client businesses, translating deep quantitative analysis into strategy that senior stakeholders can act on. Leveraging strong applied statistics across segmentation, uplift and propensity modeling, forecasting, and profitability analytics, you will help clients mine their data, surface opportunities, and design the experiments and attribution models that prove what works. The preliminary responsibilities will be on customer and marketing analytics: shaping audience and distribution strategy and measuring tactics end-to-end while partnering with execution teams rather than owning execution yourself. You will build trusted relationships, tell a clear story with data, and work fluently across a modern analytics stack including Azure, Databricks, and Python alongside established Oracle and SAS environments. Responsibilities · Serve as an embedded, end-to-end analytics partner, owning strategy, analysis, and measurement while working closely with execution teams rather than executing directly. · Build and maintain analytical models including segmentation, uplift and propensity models, forecasting, and profitability analytics to surface and prioritize opportunities. · Define KPIs and funnel metrics, and maintain a singular, ongoing view of performance from measurement pipeline through tactical outcome. · Partner with execution teams to jointly design experiments, then own the attribution models and monitoring that measure their results. · Mine the client base to understand behaviors, portfolio, and market context, and advise on audience strategy and how to move clients toward specific actions. · Present findings to executive stakeholders, telling a clear story with data and becoming a consultant the business depends on for strategic decisions. · Operate independently across a modernizing stack (Azure, Databricks, Python, Oracle, SAS) and within client MarTech ecosystems such as Salesforce and Adobe Analytics. Qualifications · Undergraduate degree in statistics, economics, mathematics, or a related quantitative field; master's or equivalent applied experience preferred. · 7+ years in applied analytics, marketing science, or a comparable quantitative consulting or strategy role, including senior stakeholder-facing work. · Strong applied statistics expertise across segmentation, uplift and propensity modeling, forecasting, and profitability analytics. · Hands-on experience designing experiments, building attribution models, and measuring performance end-to-end. · Proficiency in Python for analytics with experience across cloud platforms such as Azure and Databricks; Oracle and SAS familiarity an asset. · Ability to translate complex analysis into a clear narrative for technical and non-technical audiences. · Self-starter able to operate independently and own engagements end-to-end within a lean, senior team. · Experience with marketing technology such as Salesforce and Adobe Analytics is an asset.

What you’ll do

Partner with client businesses to develop analytical strategies, build models, define performance metrics, and measure outcomes through experiments and attribution. Analyze customer and marketing data, advise on audience strategy, and present actionable findings to senior stakeholders.

Requirements

Requires an undergraduate degree in a quantitative field and at least seven years of applied analytics, marketing science, or comparable consulting experience; a master's degree or equivalent applied experience is preferred. Candidates should have strong applied statistics and hands-on experience with experiments, attribution, Python, and cloud analytics platforms, along with the ability to communicate findings and work independently.

Listed skills

• Microsoft Azure · Preferred
• Oracle · Preferred
• Python · Preferred
• Forecasting · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• Applied Statistics
• Customer Analytics
• Marketing Analytics
• Segmentation
• Uplift Modeling
• Propensity Modeling
• Forecasting
• Profitability Analytics
• Experiment Design
• Attribution Modeling
• KPI Definition
• Python
• Azure
• Databricks
• Oracle
• SAS

Job areas

• Data & Analytics
• Technology
• Marketing
• Consulting

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