Job description

About Improvado Improvado is a marketing data and AI analytics platform for enterprise brands and agencies. We connect 500+ marketing and sales data sources, normalize them through a governed data model, and put an AI agent on top so marketing teams can ask questions of their own data without going through BI or IT. What we sell: extraction and 500+ connectors; data transformation (SQL and dbt-compatible); Marketing Data Governance — taxonomy, naming, data-quality rules; warehouse and storage; dashboards; AI agent and MCP access to live marketing data; DECS, data engineering as a service on a 2 to 4 week SLA. Who buys it: • Economic buyers: CMO, VP Marketing, VP Demand Gen, agency COO and ops leadership • Technical evaluators: VP or Director of Marketing Analytics, marketing analysts, data engineering, sometimes IT and security • Segments: enterprise brands, mid-market, agencies • Verticals: pharma and healthcare, financial services, retail and eCommerce, gaming, agencies Why this role, now • Our messaging is not aligned. Positioning, web, sales pitch, product and content each carry a different version of what Improvado is. The market categorizes us as "marketing ETL" while we position as an AI marketing analytics platform. That gap costs us deal quality and price. • The category is crowded and moving fast. Battlecards exist but decay, and nobody owns keeping them true. • Attribution, incrementality and causal measurement is where deals are won and lost — and it is the hardest thing to argue credibly without hand-waving. • Analyst firms are a silent blocker. When an enterprise buyer asks Gartner or Forrester about Improvado, there is no briefed narrative waiting for them. You would not start from a blank page. We already have a positioning registry, roughly 11 documented buyer personas, a pain-point and value-prop library, competitor intel reports and about 10 battlecards. They are inconsistent and under-used. Your first job is to make one version true and make people use it. What you'll own • Positioning and messaging. Core positioning, category framing and message hierarchy across ICPs and personas. ICP and persona documentation that is used, not filed. The messaging layer of new product and feature launches, including the internal rollout that makes the field able to sell it. • Competitive intelligence and win/loss. A structured win/loss interview program with quarterly readouts. Living battlecards. Monitoring competitor positioning, pricing and product moves, and driving our counter-positioning. • Sales enablement. The core pitch narrative, demo storyline and objection-handling framework. The messaging components of new-rep onboarding. A standing weekly cadence with Sales leadership — not quarterly check-ins. Direct support on competitive and enterprise deals: RFPs, security and technical evaluations, displacement plays. • Market and analyst relations. The Improvado narrative for Gartner, Forrester and category influencers, so that a buyer's analyst call becomes an accelerator rather than a blocker. Feeding structured market and buyer insight back into product prioritization. What we're looking for • 4 to 8 years in product marketing, or an equivalent blend of product marketing with marketing analytics, marketing or data ops, sales engineering, analytics consulting, or product management on a data product. • Category fluency in marketing data and measurement: attribution models, incrementality testing, MMM, data governance and taxonomy, warehouses and ETL/ELT — including where each is honestly weak. • B2B SaaS sold to marketing or data buyers. Marketing analytics, martech, measurement, CDP, BI and data-infrastructure vendors are the reference profile. • Proof you have shipped, not just documented: positioning that changed the website and the pitch, battlecards reps actually opened, a win/loss program that survived past its second quarter. • Writing that survives contact with a skeptical technical buyer — claim and evidence, not adjective stacking. • Comfort operating close to Sales: weekly cadence, deal reviews, live calls, direct feedback. Nice to have: hands-on SQL, dbt or BI, enough to validate a claim yourself. Experience marketing AI capabilities without hype. Analyst-relations experience. Agency-side or enterprise-brand background. Vertical depth in pharma and healthcare, financial services, retail and eCommerce, or gaming. Not a fit if: your product marketing has been mostly launch logistics and content calendars; you need a fully staffed team and a finished brand system to be effective; you are uncomfortable being told in a deal review that your messaging did not work. How we measure success • Win rate in competitive deals against our top 3 named competitors, baseline set in month 1. • Stage-to-stage conversion on open opportunities, particularly discovery to demo to proposal. • Time-to-first-deal for new reps. • Field adoption of enablement assets — measured, not assumed. • Win/loss program running on cadence, with themes that visibly change product and GTM decisions. • Message consistency: a quarterly audit across web, deck, product UI and content. What We Offer • Remote-first environment • Competitive compensation • Medical and dental benefits • 401K plan • Unlimited PTO • Paid holidays • Professional development reimbursement

Requirements

Must-haves

Economic buyers: CMO, VP Marketing, VP Demand Gen, agency COO and ops leadership
Technical evaluators: VP or Director of Marketing Analytics, marketing analysts, data engineering, sometimes IT and security
Segments: enterprise brands, mid-market, agencies
Verticals: pharma and healthcare, financial services, retail and eCommerce, gaming, agencies
Why this role, now
The category is crowded and moving fast. Battlecards exist but decay, and nobody owns keeping them true.
Attribution, incrementality and causal measurement is where deals are won and lost — and it is the hardest thing to argue credibly without hand-waving.
Analyst firms are a silent blocker. When an enterprise buyer asks Gartner or Forrester about Improvado, there is no briefed narrative waiting for them.
What you'll own
4 to 8 years in product marketing, or an equivalent blend of product marketing with marketing analytics, marketing or data ops, sales engineering, analytics consulting, or product management on a data product.
Category fluency in marketing data and measurement: attribution models, incrementality testing, MMM, data governance and taxonomy, warehouses and ETL/ELT — including where each is honestly weak.
B2B SaaS sold to marketing or data buyers. Marketing analytics, martech, measurement, CDP, BI and data-infrastructure vendors are the reference profile.
Proof you have shipped, not just documented: positioning that changed the website and the pitch, battlecards reps actually opened, a win/loss program that survived past its second quarter.
Writing that survives contact with a skeptical technical buyer — claim and evidence, not adjective stacking.
Comfort operating close to Sales: weekly cadence, deal reviews, live calls, direct feedback.
How we measure success
Win rate in competitive deals against our top 3 named competitors, baseline set in month 1.
Stage-to-stage conversion on open opportunities, particularly discovery to demo to proposal.
Time-to-first-deal for new reps.
Field adoption of enablement assets — measured, not assumed.
Win/loss program running on cadence, with themes that visibly change product and GTM decisions.
Message consistency: a quarterly audit across web, deck, product UI and content.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aisql

Platform: jazzhr
Effort: medium

Benefits

Benefits detected in the listing.

Dental

401k

Pto

Professional Development