wealthsimple

Data Scientist, Sales

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Older posting · still listed ·

  • remote
  • FullTime
  • Data & Engineering
  • Remote (Canada)
  • Toronto, Canada
Work arrangement
remote
Employment
FullTime
Stated compensation
CAD 123,200–154,000 1 YEAR
Stated sponsorship
Not stated
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Requirements

3 with evidence6 unclear or unstated
Remote policy
remote
“Provider workplace type: remote”
From employer posting metadata
Geographic scope
CA
“Provider location: Remote (Canada) • Provider location: Toronto, Canada ”
From employer posting metadata
Minimum experience (years)
2
“2–4 years of experience in a Data Science, Decision Science, or Analytics Engineering role.”
See matched passage in original description ↓
Employment type
FullTime
Compensation
CAD 123,200–154,000 1 YEAR
6 fields not stated in this posting
Employer sponsorship
Not specified
Degree requirement
Not specified
Security clearance
Not mentioned
Timezone / hours
Not specified
Citizenship requirement
Not mentioned
Work authorization
Not specified

These details were not stated in the tracked posting. Confirm with the employer.

READ IT IN THE EMPLOYER’S OWN WORDS

Original job description

Reading view uses normalized posting text so exact evidence excerpts can be highlighted.

Build something people love Wealthsimple is Canada’s leading financial innovator. The company offers a full suite of simple, sophisticated financial products across managed investing, do-it-yourself trading, cryptocurrency, tax filing, spending and saving. Wealthsimple currently serves more than 4 million Canadians and holds over $169 billion in assets under administration. The company was founded in 2014 by a team of financial experts and technology entrepreneurs, and is headquartered in Toronto, Canada. We're proud of what we've built — and we're just getting started. Read our Culture Manual and learn more about how we work.
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About the team  The Data Science team at Wealthsimple consists of data scientists with diverse educational backgrounds such as physics, computer science, engineering and business. The Sales Data team is responsible for supporting the entire Commercial team through data modeling, pipelining, dashboarding, experimentation, analyses, and machine learning models. Our stakeholder teams are diverse, including Financial Planners & Advisors, Business Development Representatives, Revenue Operations and Commercial Systems. We build data solutions that directly optimize the sales process, improve lead scoring and routing, boost lifecycle marketing and drive revenue growth for Wealthsimple’s core metrics.  We are looking for a Data Scientist with strong foundational skills in both data engineering and applied data science. In this role, you will act as a generalist bridging the gap between data architecture and applied science. You will take ownership of stakeholder relationships while designing, building, and maintaining robust data models and pipelines to scale our Commercial operations. In this role, you will have the opportunity to: Design, build, and maintain accurate, reliable data pipelines and commercial data marts using modern tools like dbt and Airflow. Balance technical execution roughly 50/50 between data engineering/pipelining and applied data science/analytics. Optimize commercial operations by enhancing lead generation, lead routing, and sales efficiency using sparse or multi-modal data sources. Develop, implement, and maintain metrics, dashboards, and predictive models that directly impact top-line business goals. Manage direct relationships with business stakeholders across Sales Operations, Commercial Leadership, Revenue Operations, and Product. Conduct exploratory analyses and design experiments (e.g., A/B testing) to test hypotheses and improve go-to-market strategies. Collaborate closely with teammates to raise data quality standards, share knowledge, and improve core data architecture. What you bring: 2–4 years of experience in a Data Science, Decision Science, or Analytics Engineering role. Strong proficiency in SQL & Python, with hands-on experience building and maintaining data pipelines using tools like dbt and Airflow. Data Engineering Aptitude: Comfortable working with data architecture, designing clean data models, and managing data pipeline flows. Stakeholder Relationship Building: Ability to communicate technical findings, build trust, and align priorities with business stakeholders independently. Applied Statistics & ML Foundations: Practical understanding of foundational statistical concepts, experimentation (A/B testing), and machine learning techniques applied to real-world business problems. Comfort working with messy, sparse, or unstructured datasets to extract actionable insights about client behavior and sales processes. Why Wealthsimple? 🌸 Top-tier health benefits and life insurance 📈 Long-term group savings with employer match, through Wealthsimple for Business 🌴 20 vacation days, 4 wellness days, and unlimited sick and mental health days per year* ✈️ 90 days away: work outside Canada for up to 90 days per year* 👥 Employee resource groups, including Rainbow (2SLGBTQ), Women of WS, and Black at WS 🌎 We are a hybrid team with over 1,500 employees across North America. The people are one of the best parts of working here: you'll collaborate with incredibly talented, curious, and driven teammates who are deeply committed to doing great work. *Unlimited paid sick days, Wellness Days and the 90 day away program do not apply to certain roles. ICYMI Technology & Innovation at Wealthsimple: We move quickly and build thoughtfully. That means we're always looking for better ways to work — whether that's new tools, AI, or rethinking how we approach a problem. We don't expect you to have all the answers, but we do expect curiosity and a willingness to evolve alongside the products we're building. Inclusion Statement: We're building products for a diverse world, and we need a diverse team to do it well. We strongly encourage applications from everyone, regardless of race, religion, colour, national origin, gender, sexual orientation, age, marital status, or disability status. Accessibility Statement: We're committed to an accessible hiring experience. If you need any accommodations throughout the interview process, please let us know — we'll work with you to make sure you have what you need. We also welcome any feedback on how we can better accommodate candidates with accessibility needs. AI in Hiring: We may use artificial intelligence (AI) tools to support parts of our hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our team but don't replace human judgment – all final hiring decisions are made by people. If you have questions about how your data is used, reach out to us.
Original formatting and links

Build something people love

Wealthsimple is Canada’s leading financial innovator. The company offers a full suite of simple, sophisticated financial products across managed investing, do-it-yourself trading, cryptocurrency, tax filing, spending and saving. Wealthsimple currently serves more than 4 million Canadians and holds over $169 billion in assets under administration. The company was founded in 2014 by a team of financial experts and technology entrepreneurs, and is headquartered in Toronto, Canada.

We're proud of what we've built — and we're just getting started. Read our Culture Manual and learn more about how we work.

About the team

 

The Data Science team at Wealthsimple consists of data scientists with diverse educational backgrounds such as physics, computer science, engineering and business. The Sales Data team is responsible for supporting the entire Commercial team through data modeling, pipelining, dashboarding, experimentation, analyses, and machine learning models. Our stakeholder teams are diverse, including Financial Planners & Advisors, Business Development Representatives, Revenue Operations and Commercial Systems. We build data solutions that directly optimize the sales process, improve lead scoring and routing, boost lifecycle marketing and drive revenue growth for Wealthsimple’s core metrics.

 

We are looking for a Data Scientist with strong foundational skills in both data engineering and applied data science. In this role, you will act as a generalist bridging the gap between data architecture and applied science. You will take ownership of stakeholder relationships while designing, building, and maintaining robust data models and pipelines to scale our Commercial operations.

In this role, you will have the opportunity to:

  • Design, build, and maintain accurate, reliable data pipelines and commercial data marts using modern tools like dbt and Airflow.

  • Balance technical execution roughly 50/50 between data engineering/pipelining and applied data science/analytics.

  • Optimize commercial operations by enhancing lead generation, lead routing, and sales efficiency using sparse or multi-modal data sources.

  • Develop, implement, and maintain metrics, dashboards, and predictive models that directly impact top-line business goals.

  • Manage direct relationships with business stakeholders across Sales Operations, Commercial Leadership, Revenue Operations, and Product.

  • Conduct exploratory analyses and design experiments (e.g., A/B testing) to test hypotheses and improve go-to-market strategies.

  • Collaborate closely with teammates to raise data quality standards, share knowledge, and improve core data architecture.

What you bring:

  • 2–4 years of experience in a Data Science, Decision Science, or Analytics Engineering role.

  • Strong proficiency in SQL & Python, with hands-on experience building and maintaining data pipelines using tools like dbt and Airflow.

  • Data Engineering Aptitude: Comfortable working with data architecture, designing clean data models, and managing data pipeline flows.

  • Stakeholder Relationship Building: Ability to communicate technical findings, build trust, and align priorities with business stakeholders independently.

  • Applied Statistics & ML Foundations: Practical understanding of foundational statistical concepts, experimentation (A/B testing), and machine learning techniques applied to real-world business problems.

  • Comfort working with messy, sparse, or unstructured datasets to extract actionable insights about client behavior and sales processes.

 

Why Wealthsimple?

🌸 Top-tier health benefits and life insurance

📈 Long-term group savings with employer match, through Wealthsimple for Business

🌴 20 vacation days, 4 wellness days, and unlimited sick and mental health days per year*

✈️ 90 days away: work outside Canada for up to 90 days per year*

👥 Employee resource groups, including Rainbow (2SLGBTQ), Women of WS, and Black at WS

🌎 We are a hybrid team with over 1,500 employees across North America. The people are one of the best parts of working here: you'll collaborate with incredibly talented, curious, and driven teammates who are deeply committed to doing great work.

*Unlimited paid sick days, Wellness Days and the 90 day away program do not apply to certain roles.

ICYMI

Technology & Innovation at Wealthsimple: We move quickly and build thoughtfully. That means we're always looking for better ways to work — whether that's new tools, AI, or rethinking how we approach a problem. We don't expect you to have all the answers, but we do expect curiosity and a willingness to evolve alongside the products we're building.

Inclusion Statement: We're building products for a diverse world, and we need a diverse team to do it well. We strongly encourage applications from everyone, regardless of race, religion, colour, national origin, gender, sexual orientation, age, marital status, or disability status.

Accessibility Statement: We're committed to an accessible hiring experience. If you need any accommodations throughout the interview process, please let us know — we'll work with you to make sure you have what you need. We also welcome any feedback on how we can better accommodate candidates with accessibility needs.

AI in Hiring: We may use artificial intelligence (AI) tools to support parts of our hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our team but don't replace human judgment – all final hiring decisions are made by people. If you have questions about how your data is used, reach out to us.

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