Field Marketing Manager, Network & Information Services Jobs in Ontario
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Ontario
About the role
- Reporting to the Director of Product Analytics, the Product Analytics Manager will champion analytical initiatives designed to advance Jobber’ subscription product capabilities (desktop & Mobile App), user experience and product marketing strategy
- The purpose of the role is to ensure that Product Development, Experimentation and Product Marketing teams have all the information they need to make design, experience and GTM decisions that drive success of the Jobber product in the market, as well as measure the success of those initiatives
- This is a high impact, hands-on individual contributor role
- What you will do is own a product domain analytically, from the framing of the question through to the recommendation that lands in a product or GTM decision
- Design, run, and analyze experiments; including navigating the real constraints of a SaaS environment: low traffic, small samples, multiple concurrent product changes, and the temptation to call significance too early
- Apply causal inference methods where clean A/B testing isn’t possible; difference-in-differences, regression discontinuity, synthetic controls, CUPED/variance reduction techniques
- Build customer segmentation and lifecycle models that feed directly into product and GTM decisions, not just sit in a dashboard
- Partner with Product, Design, and GTM stakeholders to translate ambiguous business questions into analytical plans; and push back when the question itself needs reframing
- Develop scenario and sensitivity models that let business leaders stress-test assumptions before committing to a strategy
- Contribute to Jobber’s experimentation culture by consulting on experimental design, statistical best practices, and measurement frameworks across teams
- Work with Analytics Engineering to define data requirements and ensure the instrumentation exists to answer the questions that matter
- Support experimentation initiatives by providing requirements for experimental design and successful measurement and Jobber’s general culture of experimentation by providing consultation on best practices (e.g. applied statistics, tooling, quantity of experiments, importance statistical significance)- End-to-end experimentation experience: you’ve designed the test, chosen the metrics, monitored for novelty effects, handled early-stopping decisions, and communicated the results to a non-technical audience
- Bayesian methods or probabilistic modeling for situations where frequentist approaches don’t fit
- The ability to simplify without losing accuracy, you can take a technically complex result and give a stakeholder exactly what they need to make a decision, nothing more
- To have a strong and confident communication style. You have the ability to actively listen, empathize and consult with stakeholders, and you can take something complex and difficult and make it easy to digest
- Be curious and relentless. You are comfortable seeking information independently, solving conceptual problems, corralling resources and delivering results
- Strong analytical SQL: complex joins, window functions, cohort analysis, funnel construction; you write it to think, not just to pull data
- Fluency with causal inference - you understand when an A/B test isn’t possible or appropriate, and you have a toolkit for those situations
- Be comfortable in an ambiguous and fast-paced environment. We’re growing fast and things are changing every day – what worked yesterday might not anymore
- Python for analysis and modeling, pandas, statsmodels, scikit-learn
- Comfort with uncertainty: you communicate confidence intervals, flag where signal is thin, and push back on requests for false precision
- Deep, hands-on experience in product analytics for a SaaS or marketplace product you’ve worked in a product development cycle, not just reported on outcomes after the fact
- Informal leadership experience, mentoring peers, running knowledge-sharing sessions, helping junior analysts level up, even without a formal title
- Experience with Monte Carlo simulation or scenario modeling for business planning problems
- Foster a collaborative and supportive work environment by actively participating in knowledge-sharing sessions and seizing opportunities to mentor and guide peers, contributing to the professional growth and development of the team
About Jobber
Software Development
1001-5000
Jobber is an award-winning software for small home service businesses.
Unlike spreadsheets or pen and paper, Jobber keeps track of everything in one place and automates day-to-day operations, so small businesses can run smoothly and provide five-star service at scale.
Jobber is used by 300,000 home service pros to serve over 27 million properties in more than 60 countries. The company continually ranks as one of Canada's fastest-growing and most innovative companies by Canadian Business and Macleans, The Globe and Mail, Fast Company, and Deloitte