Engineering Manager, Data Science

Mural; Remote; CAD 110,000 - 150,000; Full time

Job summary

A leading collaborative platform in Canada is seeking a Data Science Manager to lead the delivery of ML systems and foster a high-performing team. In this hands-on role, you will design recommendation engines and predictive models that significantly impact business metrics. Candidates should have over 6 years of experience in ML, proven leadership skills, and a strong technical foundation in Python. This remote position offers a unique opportunity to influence product development directly and drive business success.

Qualifications

• 6+ years building and deploying consumer-facing ML systems.
• 2+ years leading or managing data scientists or ML engineers.
• Strong Python skills and experience with ML platforms.

Responsibilities

• Lead the design and delivery of production ML systems.
• Define model success criteria and track performance.
• Hire and coach data scientists to maintain high expectations.

Skills

Deep ML experience

Leadership experience

Technical fluency in Python

Business orientation

Pragmatic delivery mindset

AI-driven development practices

Tools

Databricks

Job description

Location

Canada Remote

Employment Type

Full time

Location Type

Remote

Department

Engineering

ABOUT THE TEAM

The Data Science team builds the predictive engines and analytical capabilities that power decisions across Mural. We're a small team within the Data Organization, delivering data products—recommendation systems, churn models, experimentation frameworks—to R&D, Finance, and GTM. Our work directly influences how millions of users discover value in Mural and how the business grows. We operate with the autonomy of a small team and the reach of a company with a large, active user base.

YOUR MISSION

As Data Science Manager, you will own the delivery and evolution of Mural's data products while building a high-performing team. This is a player-coach role—you'll stay hands-on with model development and system design while setting technical direction and growing your team's capabilities. You will partner closely with R&D, Finance, and GTM stakeholders to turn business problems into deployed models that move metrics. Your success will be measured by whether the models you ship actually improve retention, conversion, and revenue—not by the sophistication of the approach.

WHAT YOU'LL DO

• Ship production ML systems: Lead the design and delivery of recommendation engines, churn prediction models, and messaging experimentation infrastructure—staying hands-on in code while your team scales

• Own outcomes end-to-end: Define model success criteria, track performance across all deployed models, and iterate until business metrics move—not just until models deploy

• Build and develop the team: Hire strong data scientists, coach them through technical and career challenges, and maintain high expectations for both craft and impact

• Partner across the business: Work directly with R&D, Finance, and GTM to identify high-leverage problems, scope solutions that can ship incrementally, and ensure data products get adopted—not just delivered

• Set technical direction: Make pragmatic decisions about tooling, architecture, and methodology that balance near-term delivery with long-term maintainability

WHAT YOU'LL BRING

• Deep ML experience: 6+ years building and deploying consumer-facing ML systems—recommendation engines, churn models, or similar. You've shipped models that ran in production at scale, not just notebooks.

• Leadership experience: 2+ years leading or formally managing data scientists or ML engineers. You've built teams, not just participated in them.

• Technical fluency: Strong Python skills; experience with Databricks or comparable ML platforms. Comfortable across the full lifecycle—experimentation, feature engineering, model training, deployment, monitoring.

• Business orientation: Track record of translating ambiguous business problems into measurable ML solutions. You care whether the model moved the metric, not just whether it trained.

• Pragmatic delivery mindset: You know when to ship an MVP to get feedback and when to invest in robustness. You edit scope ruthlessly rather than letting projects bloat.

• An outcome-oriented and highly experimental interest in AI-driven development practices: You actively incorporate AI tools into your workflow and expect the same from your team.

Nice to have

• Experience with experimentation platforms or causal inference methods

• Background in subscription/SaaS businesses with retention and conversion challenges

• Familiarity with TypeScript or production engineering practices

Equal Opportunity

We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.