Market Intelligence Specialist

Fin-X

Market Intelligence Specialist transforming data into actionable insights for revenue decisions at Fin-X healthtech. Monitoring competitors and analyzing market trends in SaaS and fintech health sectors.

Posted 7/3/2026full-timeRemote • 🇧🇷 BrazilMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates strong analytical and synthesis abilities in competitive intelligence and market research within the SaaS and fintech sectors. Proficient in utilizing AI tools and low-code automation for data collection and analysis to drive strategic insights and decision-making.

Highest-signal resume keywords

Advanced Excel / Google SheetsBasic to Intermediate SQLPractical Use of AI (GPT-4, Claude)Low-Code Automation Tools (Zapier, Make, n8n)Qualitative Research Methodology

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data AnalysisPredictive ModelingBasic PythonCohort AnalysisMarket ResearchCompetitive IntelligenceData CleaningWeb ScrapingStructured SynthesisModeling

Soft Skills

Executive CommunicationAutonomyProactivityIntellectual FlexibilityTolerance for AmbiguityStructured CuriositySystems Thinking

Tools & Technologies

Power BILooker StudioDataRobotBigMLZapierMakeN8n

Industry Keywords

SaaSFintechHealthcareB2B Market ResearchCompetitive MonitoringTotal Addressable Market (TAM)Win/Loss AnalysisCustomer InsightsMarket ShareChurn Risk

Tech Stack

Tools & technologies

PythonSQL

About the role

Key responsibilities & impact

Continuously monitor the SaaS + fintech ecosystem for healthcare: Rivio, Upflux, Osigu, Noxtec, TOTVS Saúde and new entrants
Build and operate an active monitoring system with defined sources, update cadence and output format — not ad-hoc research
Use AI to scale monitoring: news-tracking agents, competitor job boards, reviews and industry publications
When a competitor releases a feature, changes pricing, enters a Fin-X account or receives funding — you deliver the context to the Director before the market notices
Own the win/loss process — from playbook design to quarterly synthesis with actionable patterns
Conduct structured interviews with prospects and lost customers, neutrally and without commercial-executive bias
Categorize loss reasons by dimension: product, price, sales process, timing, competition and inertia
Automate transcription and categorization with AI to identify clusters and patterns at scale
Maintain an up-to-date map of the Brazilian hospital TAM (Total Addressable Market): networks, independents, by size, specialty and region
Cross-reference with the current customer base to identify whitespace — where Fin-X is not yet present but should be
Feed Commercial Planning's territory planning with structured data, not estimates
Automate collection of public data: CNES, ANS and hospital industry publications
Keep market share up to date and identify market opportunities
Map the TAM of the installed base: for each customer, what revenue headroom remains uncaptured — which products they don't use, how many units are not yet deployed, and potential take rate
Produce an upsell-readiness model: which customers have the profile, health and timing for expansion — combining product adoption, health score and financial behavior
Identify churn-risk signals in advance: drops in adoption, atypical financial behavior, stakeholder changes — before CS sees the problem
Deliver a prioritized monthly list of expansion candidates to CS and Sales — with estimated revenue potential and a recommended outreach window
Provide CS with risk intelligence: prioritized monitoring accounts with context for why they are on alert — not just the score, but the signal that generated it
Collect and maintain market benchmarks: typical win rate for B2B SaaS in healthcare, sales cycle by company size, take rate of health fintechs, NRR of similar platforms and signals that feed the product roadmap
When the Director needs to know if Fin-X's pricing is correct or if churn is above expectations — you have the benchmark
Identify market signals that inform the roadmap — recurring demands in win/loss, competitor features gaining traction, gaps that generate systematic loss
Produce quarterly cohort analysis that validates or questions the current ICP: which customer vintages have higher LTV, lower churn, greater expansion
Identify what the best customers have in common — and what the worst have in common — to refine qualification criteria
Supply Commercial Planning with an evidence-based ICP, not intuition
Build and operate competitive monitoring agents that deliver a structured weekly digest to the Director
Use LLMs (GPT-4, Claude) to process and synthesize large volumes of interviews, reviews and documents
Implement automations with low-code tools (Zapier, Make, n8n) to collect and structure market data
Build simple predictive models with low-code ML tools to support expansion and ICP decisions
Keep an external intelligence dashboard automatically updated — share of voice, competitive movement, evolution of the TAM

Requirements

What you’ll need

Degree in Business Administration, Economics, Engineering, Data Science or related fields — the degree matters less than analytical and synthesis ability
Experience across at least two of the following contexts: strategy consulting, competitive intelligence at a technology company, B2B market research, strategy/planning in SaaS, or commercial BI
Exposure to the healthcare sector is a plus — but not a blocker if the analytical profile is strong
Advanced Excel / Google Sheets: modeling, pivot tables, cohort analysis
Basic to intermediate SQL: able to extract and join data without depending on engineering
Practical use of AI at work: GPT-4, Claude, Perplexity — not as a curiosity, but as a daily tool
Low-code automation tools: Zapier, Make or n8n — for building flows without developer support is a plus
Qualitative research methodology: interview script design, unbiased conduct, structured synthesis
Structured curiosity: does not get lost in endless research — defines what needs to be known, searches efficiently and delivers insight
Tolerance for ambiguity: works well with partial data and communicates the confidence level of deliverables
Executive communication: turns complex analysis into three slides the CEO understands and can act on
Autonomy and proactivity: does not wait to be asked — monitors the market and surfaces signals before the question arises
Intellectual flexibility: changes hypotheses when data contradicts them — does not defend the analysis, defends the truth
Systems thinking: understands that intelligence serves decision-making, not reporting — the goal is impact, not volume of output
Basic Python: for collection automations, data cleaning and simple scripts
Visualization tools: Power BI, Looker Studio or similar
Predictive modeling with low-code ML platforms (DataRobot, BigML or equivalent)
Basic web scraping for public data collection

Benefits

Comp & perks

Performance reviews: growth based on your development
Onboarding kit
Fin-X Day: a semiannual gathering for Culture & Strategy alignment
Access to Fiibo: exclusive perks to make daily life easier
Welhub: a wellbeing platform to support your physical and mental health
Company recess