Head of Data Science & AnalyticsAbout Big Viking Games
Big Viking Games is a Canadian gaming company focused on building, operating, and growing engaging online game experiences. Our teams work across product, technology, live operations, monetization, player experience, and content to create games and communities that last.
We are entering a new phase of growth and modernization, with a focus on stronger data visibility, better decision-making, improved operational discipline, and practical adoption of AI across the business.
This is a hybrid role with three (3) days in office.
About the Role
We are creating a new Head of Data Science & Analytics role to build and lead the data function across Big Viking Games. This is a hands-on leadership role for someone who can operate strategically, but also roll up their sleeves and personally build the analytics foundation.
The right person will be comfortable working as a solo operator at first, owning the work directly before building out the team over time. This role will be responsible for transforming raw data into actionable insights that improve decision-making across product, live operations, monetization, marketing, finance, player experience, and executive leadership.
We are also looking for someone who understands how modern AI can materially improve analytics, workflows, reporting, automation, and productivity. Experience implementing AI tools, agentic workflows, copilots, or automation systems will be highly valued.
This is a newly-created role with significant opportunity to shape the data strategy, operating model, tooling, reporting structure, and future team.
Responsibilities
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Build and own the company’s data science and analytics function from the ground up.
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Partner with executive leadership, product, live operations, marketing, finance, and technology teams to turn business questions into clear insights, recommendations, and action plans.
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Develop the core analytics roadmap across player behavior, retention, engagement, monetization, game health, content performance, live operations, user segmentation, forecasting, and business performance.
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Personally perform hands-on analysis, including SQL querying, dashboarding, data modeling, KPI development, reporting, statistical analysis, and business case development.
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Improve the quality, consistency, and reliability of company metrics, reporting, and data definitions.
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Identify gaps in data collection, instrumentation, data architecture, reporting, and operational visibility.
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Partner with engineering and data stakeholders to improve data pipelines, data quality, data governance, and access to trusted datasets.
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Design dashboards and executive reporting that help leaders understand what is happening, why it is happening, and what should be done next.
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Lead analysis on player lifecycle, cohort behavior, monetization trends, feature performance, content performance, game economy, and live-service operations.
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Support experimentation, A/B testing, measurement frameworks, and decision-ready analysis for product and business initiatives.
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Implement practical AI-driven solutions to accelerate analytics workflows, automate repetitive reporting, improve insight generation, and support better business decisions.
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Explore and deploy AI agents, copilots, workflow automations, and other tools that improve productivity across analytics, operations, product, and leadership reporting.
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Establish best practices for responsible AI use, data privacy, analytical rigor, documentation, and repeatable workflows.
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Over time, hire, coach, and lead a high-performing data science, analytics, and/or BI team.
Nice to Have
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Experience with Snowflake or similar modern cloud data platforms.
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Experience with gaming analytics, live-service games, virtual economies, content performance, player segmentation, in-game monetization, or player lifecycle analytics.
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Experience with BI tools such as Looker, Tableau, Power BI, Sigma, Mode, Metabase, or similar platforms.
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Experience with dbt, data modeling, data governance, experimentation platforms, or modern analytics engineering practices.
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Experience building AI agents or automated workflows using tools such as ChatGPT, Claude, LangChain, Zapier, Make, n8n, Retool, or internal workflow automation tools.
Experience building a data function, hiring analysts, or scaling a small data team.
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Ideal Candidate Profile
The ideal candidate is a builder, not just a manager. They have the seniority to set strategy and influence executives, but the humility and capability to do the work themselves. They are commercially minded, technically credible, and comfortable working in an environment where they may need to create structure from ambiguity.
They understand that analytics is not just reporting. It is a decision-making function. They can identify what matters, build the systems to measure it, explain what the data means, and help the business act on it.
They are also forward-looking in how they use AI. They should be able to bring practical, usable AI adoption into the company, not just talk about it conceptually.
Requirements
Qualifications
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8+ years of experience in data science, analytics, business intelligence, product analytics, or a related data discipline.
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Experience operating in a gaming, digital product, SaaS, consumer technology, marketplace, or live-service environment.
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Strong hands-on analytical skills, including advanced SQL and experience with Python, R, or similar analytical tools.
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Proven ability to translate ambiguous business problems into structured analysis, clear recommendations, and executive-level narratives.
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Experience building dashboards, reports, KPI frameworks, and decision-support tools for senior leadership.
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Strong understanding of product analytics, user behavior, retention, engagement, monetization, segmentation, cohort analysis, forecasting, and experimentation.
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Experience working with data engineering, product, finance, marketing, and executive teams.
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Experience implementing or adopting AI tools, GenAI, copilots, agentic workflows, workflow automation, or AI-enabled analytics processes.
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Ability to operate independently in a hands-on capacity before a larger team is built.
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Strong communication skills, with the ability to explain complex analysis in clear business language.
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Demonstrated ability to build structure in an environment where data, processes, tooling, or reporting may still be maturing.
Benefits
Compensation
The expected compensation range for this role is $190,000 to $230,000 CAD, based on experience, qualifications, and overall fit.
