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At Forbes Advisor, our mission is to help readers turn their aspirations into reality. We arm people with trusted advice and guidance, so they can make informed decisions they feel confident in and get back to doing the things they care about most. We are an experienced team of industry experts dedicated to helping readers make smart decisions and choose the right products with ease.

Forbes Advisor boasts decades of experience across dozens of geographies and teams, including Content, SEO, Business Intelligence, Finance, HR, Marketing, Production, Technology and Sales. The team brings rich industry knowledge to Forbes Advisor's global coverage of consumer credit, debt, health, home improvement, banking, investing, credit cards, small business, education, insurance, loans, real estate and travel.

We're hiring an Insights Manager to lead a small team and deliver high‑impact, decision‑ready analytics across the business. You'll sit in the central Analytics function, partnering cross‑functionally with Data Engineering, BI and Product to turn data into reusable, production‑quality insight products—not just one‑off dashboards.

You’ll define analytical models (e.g., LTV, attribution, consumer insight and performance), manage and QA their build with Engineering/Analytics Engineering, and elevate stakeholder decision‑making through sharp storytelling and clear recommendations. This is a hands‑on leadership role with direct line management and real influence on what we build and why.

• Define and own analytical frameworks for retention, attribution, and funnel performance; manage the build and rollout with DE/AE.
• Translate business questions into well‑scoped data problems and engineering and data science requirements; review logic and ensure documentation is clear and usable.
• Shape experiment design (A/B, holdouts), success metrics, and readouts that drive product and media decisions.

• Lead a team of analysts to deliver proactive, decision‑first analysis (not just reporting).
• Produce campaign and business narratives that explain what happened, why it happened, and what to do next.
• Partner with BI to evolve Tableau assets into scalable, self‑serve intelligence; rationalise overlapping views.

• Work closely with Data Engineering/Analytics Engineering to turn insight logic into robust dbt models with testing, versioning, and CI.
• Brief and review data requirements for new pipelines and sources (GA4, Google Ads, Meta, affiliate/partner data).
• Contribute to AI/GPT‑assisted insight (e.g., automated narratives, anomaly triage, assisted readouts) in collaboration with BI Ops and Product.

• Line‑manage 2‑3 direct reports (with potential dotted‑line mentorship of juniors): set goals, coach, review, and uplevel standards.
• Establish a repeatable delivery rhythm (intake → prioritisation → delivery → readout → productisation).
• Raise the bar on analytical quality, documentation, and reuse across the team.

• Own the stakeholder map and cadence (SEM, Product, RevOps, GMs): set expectations, align on goals, and keep a clear delivery roadmap.
• Create summaries for senior stakeholders and present findings that translate analysis into decisions; document actions, owners, and timelines.
• Triage and prioritise inbound requests against team capacity; say “no” (or “not now”) constructively, with alternatives.
• Surface risks early, align trade‑offs, and drive resolution with your team, Data Engineering/BI and business partners.

• Technical toolkit: Strong SQL and dbt (data modelling, tests, documentation); Python for analysis/modelling (pandas, scikit‑learn, NumPy); comfort with Git‑based workflows.
• BI experience: Strong with Tableau (or similar: Looker, Power BI) including performance optimisation and stakeholder‑ready design.
• Cloud experience: Experience working in GCP or similar cloud environments to deliver high quality data at scale.
• Marketing & product fluency: Working knowledge of GA4, Google Ads, Meta, and growth/monetisation levers.
• Modelling background: Practical experience with attribution, segmentation, conversion prediction, and experiment analysis.
• Storytelling & influence: Ability to frame insights as clear recommendations tied to business impact; excellent written and verbal communication.
• People leadership: Experience coaching analysts, reviewing work, and running a predictable analysis delivery process.
• Experience level: Significant experience in analytics/insights roles in data‑rich environments (typically 7–10 years). If you've built equivalent capability faster, we still want to hear from you.

• Experience in affiliate/lead‑gen or performance marketing environments.
• Exposure to LLMs/GPT for narrative generation, classification, or assisted analysis.
• Statistical depth (causal inference basics, uplift modelling, Bayesian thinking).

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