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About Cloudbyz

Cloudbyz is a Salesforce-native unified eClinical platform serving pharmaceutical, biotech, CRO, SMO, medical device, and cosmetics companies globally. Our product suite spans CTMS, eTMF, EDC, CTFM, Safety & Pharmacovigilance, and AI Agents. We are a fast-growing US-headquartered SaaS company competing in the life sciences technology market.

We are building a GTM Intelligence Lab in India — a commercial intelligence unit that powers the pipeline of our US sales team. The Head of GTM Intelligence is the founding leader of that unit and the most senior hire into the lab.

The opportunity

Cloudbyz's US sales team operates in five high-value market segments with clearly defined ICPs, established competitive dynamics, and significant untapped pipeline. The constraint is not product quality or pricing — it is the speed and precision with which the sales team can identify in-market buyers, understand their context, and engage them with relevant intelligence before a competitor does.

The Head of GTM Intelligence owns the function that eliminates that constraint. You will build and lead a team of Market Research Analysts and a Data Science Engineer in India, establishing the systems, cadences, and standards that transform raw market signals into weekly pipeline-ready intelligence delivered to US AEs.

This is a leadership role in a function that does not yet exist. You will define what it is, hire the people who run it, build the processes that make it work, and be accountable for the pipeline it generates.

The mandate: Build a commercial intelligence unit that makes Cloudbyz's US sales team the best-informed team in the eClinical market — faster to a signal, sharper on context, and more targeted in outreach than any competitor.

What you will own

• Team — Hire, develop, and lead a team of 3–6: Market Research Analysts (2–3), Data Science Engineer (1), and Content & Enablement Analyst (1). Define roles, set performance standards, and build the operating culture of the lab.
• Research agenda — Own the weekly, monthly, and quarterly intelligence roadmap. Decide which segments, signals, accounts, and competitors the team focuses on. Prioritize ruthlessly — the lab's value is in what it chooses not to research as much as what it does.
• US sales alignment — Run a standing weekly sync with the US Sales Lead and AEs. Translate commercial needs into research briefs. Route intelligence to the right rep with context. Act as the commercial intelligence partner to the sales team, not a support function.
• Signal library — Own the master GTM signal library across all five segments — pre-market, post-market, RFP, competitor, and technographic signals. Keep it current, scored, and operationally actionable.
• AI & automation strategy — Oversee the Data Science Engineer's automation roadmap. Make toolstack decisions. Set the standard for what gets automated vs. what requires human judgment. Ensure the AI layer augments analyst quality rather than replacing analytical rigor.
• Output quality — Every intelligence output that reaches a US AE carries your quality standard. You review, refine, and approve until the team's output quality is consistently high enough to run without your direct review on every piece.
• Pipeline accountability — Report on pipeline influenced by GTM intelligence monthly to VP Sales and quarterly to the CEO. Own the metrics. Know the numbers. Advocate for the lab's commercial contribution with data.

Key responsibilities

1. Team building & leadership

• Recruit, interview, and hire the founding team: Market Research Analysts and the Data Science Engineer; write job descriptions, run structured interviews, and make hiring decisions with a high bar for analytical rigor and commercial orientation.
• Onboard new hires against a 30-60-90 day plan with clear output milestones; manage performance through direct feedback, weekly 1:1s, and structured quarterly reviews.
• Build a team culture that prizes speed, specificity, and commercial accountability over research comprehensiveness; set the standard that a 3-page account brief delivered Monday morning beats a 30-page market report delivered in 6 weeks.
• Develop analysts into increasingly senior researchers; create growth paths within the lab and advocate for team members' visibility with US leadership.
• Manage team capacity and prioritization; make explicit calls on what the team works on and what it does not when demand exceeds capacity.

2. Research agenda & intelligence strategy

• Define and maintain the research agenda across all five Cloudbyz market segments: pharma, biotech, CRO, SMO, and cosmetics/personal care — plus post-market and Safety/PV as a cross-segment lens.
• Develop and own the ICP framework for each product line: CTMS, eTMF, EDC, CTFM, Safety & Pharmacovigilance, and AI Agents; update as deal outcomes and market intelligence refines the profile.
• Commission and oversee deep-dive research on white space opportunities, emerging regulatory tailwinds, and adjacent market segments that Cloudbyz is not yet selling into.
• Build and maintain the master signal library: define signal types, scoring criteria, urgency tiers, and routing rules for all five segments; review and recalibrate quarterly based on conversion data.
• Stay current on life sciences industry dynamics: M&A activity, regulatory changes (ICH E6(R3), MoCRA, EU MDR), competitive product launches, and technology adoption trends that affect Cloudbyz's market.

3. US sales alignment & intelligence delivery

• Own the relationship with the US Sales Lead and AE team; be the single point of accountability for intelligence quality, timeliness, and relevance from the India lab.
• Run a standing weekly sync with US sales — review what intelligence was acted on, what missed, which accounts are in active evaluation, and what the pipeline needs from the lab in the coming week.
• Translate AE deal context into research briefs: when an AE enters a competitive deal, the lab produces a targeted competitive brief within 48 hours; when an AE needs an account deep-dive, the lab turns it around within 24 hours.
• Establish and enforce the weekly intelligence package cadence: hot account list, signal digest, and trigger event summary delivered to every AE every Monday morning without exception.
• Build a feedback mechanism that captures how AEs use intelligence outputs and routes conversion data back into the scoring models and research agenda.

4. Competitive intelligence ownership

• Own Cloudbyz's competitive intelligence function: maintain deep, current competitive profiles.
• Produce monthly competitive battlecards for each product category — reviewed and approved before reaching AEs; ensure they reflect current product state, pricing intelligence, and known displacement angles.
• Build and maintain the technographic database of target accounts: which competitors they use, when they implemented, estimated renewal windows, and known satisfaction signals.
• Monitor competitor go-to-market moves: new partnerships, pricing changes, product launches, customer wins and losses, and sales talent movements that signal strategic shifts.
• Provide competitive intelligence input to the Cloudbyz product team and CEO as market evidence for product roadmap and positioning decisions.

5. Automation strategy & AI oversight

• Define the automation roadmap with the Data Science Engineer: prioritize which research workflows to automate first based on AE impact and analyst time savings.
• Make informed toolstack decisions: evaluate and recommend intelligence platforms, LLM providers, and data sources; balance capability against cost and data quality.
• Set standards for AI-assisted research output quality: define what requires human review before reaching an AE vs. what can be auto-routed; prevent the lab from shipping low-quality AI-generated noise at scale.
• Stay current on AI research tooling and agentic workflow capabilities; identify and pilot tools that materially improve lab output quality or speed before competitors adopt them.
• Oversee RFP monitoring infrastructure: ensure the lab detects relevant RFPs across all procurement channels with sufficient lead time for AEs to respond competitively.

6. Reporting & commercial accountability

• Define and own GTM Intelligence KPIs: pipeline influenced, signal-to-action time, AE adoption rate, competitive win rate on battlecard-supported deals, and RFP detection rate.
• Report pipeline contribution monthly to VP Sales; prepare and present quarterly business reviews to the CEO with clear attribution between lab outputs and commercial outcomes.
• Build the business case for lab investment as the function scales: headcount, tooling, and data subscriptions justified by pipeline influenced metrics.
• Maintain a research audit trail: all intelligence outputs logged, sourced, and traceable so that any deal outcome can be traced back to its originating signal or brief.
• Identify and escalate risks: competitor moves that require immediate product or positioning response, market shifts that affect segment prioritization, or data source failures that create blind spots.

Required qualifications

Leadership & function-building experience

• 7–12 years of total professional experience, with at least 3 years in a leadership role owning a revenue-contributing function — sales intelligence, revenue operations, GTM strategy, competitive intelligence, or market research in a B2B SaaS or technology company.
• Demonstrated experience building a team or function from scratch: hired the first people, defined the processes, shipped the first outputs, and scaled the operation.
• Track record of leading offshore or distributed teams with accountability to US-based commercial stakeholders — experience bridging India-based execution with US sales expectations is a strong differentiator.
• Direct experience with quota-carrying sales teams as a partner or stakeholder — understands what AEs need, how they think about pipeline, and what makes intelligence actionable vs. interesting.

Domain expertise

• Deep familiarity with B2B SaaS go-to-market: market segmentation, ICP development, competitive positioning, and demand generation strategy.
• Working knowledge of the life sciences or clinical research industry — understanding of clinical trial lifecycle, regulatory environment, and eClinical technology landscape is a material advantage; candidates without this background must demonstrate rapid domain acquisition from adjacent industries (healthtech, regulated SaaS, pharma services).
• Experience with competitive intelligence methodology: primary and secondary research, technographic analysis, win/loss analysis, and competitive battlecard development.
• Familiarity with revenue operations infrastructure: CRM (Salesforce or HubSpot), sequencing tools, intent data platforms, and data enrichment tooling.

Research & analytical skills

• Ability to synthesize large volumes of disparate information into a short, opinionated, commercially actionable brief — the key skill is judgment about what matters, not thoroughness.
• Experience working with AI-augmented research workflows: using LLMs, Perplexity, or similar tools as research accelerators while maintaining output quality standards.
• Strong written English — research outputs and briefs go directly to US sales leadership without editing; communication must be clear, crisp, and calibrated for a US enterprise sales context.
• Structured thinker: can design frameworks, scoring models, and signal libraries that a team of analysts can operate consistently without close supervision.

Preferred qualifications

• Prior experience in a pharma, biotech, CRO, medical device, or life sciences technology company in a commercial or strategy role.
• Experience building or managing a revenue operations or sales intelligence function that was measured on pipeline contribution, not research output volume.
• Familiarity with eClinical technology vendors and understanding of where incumbents have known weaknesses.
• Experience with intent data platforms (Bombora, 6sense) and procurement intelligence tools (PerAngusta, Citeline, SAM.gov monitoring).
• Experience presenting commercial strategy or competitive analysis to C-suite stakeholders — comfortable defending research conclusions and making recommendations under scrutiny.
• Prior experience working with or reporting to a US-based VP Sales or CRO from an India-based leadership role.

How success is measured

This role is measured at two levels: team output quality in the short term and pipeline contribution in the medium term.

• Pipeline influenced — Monthly dollar value of pipeline in active stages that can be traced to lab-originated intelligence — target established in Month 3, grows monthly.
• AE adoption rate — Percentage of US AEs actively using the weekly intelligence package — target 80%+ within 90 days of launch.
• Weekly package reliability — Delivered to every AE every Monday without exception — 100% on-time target from Month 2.
• Signal-to-AE time — High-urgency signals (approvals, funding, RFPs) delivered within 60 minutes of detection — target from Month 4 once automation is live.
• Competitive win rate — Win rate delta on deals where a battlecard was used vs. not — measured quarterly.
• RFP detection rate — Percentage of active RFPs in target segments detected before formal submission deadline — target 80%+ within 6 months.
• Team retention — Zero unplanned attrition in the first 12 months — the lab's institutional knowledge is concentrated in a small team.

Reporting structure & interfaces

Reports to: VP of Sales or Chief Revenue Officer, Cloudbyz Inc. (US-based). This is a commercial function, not a marketing or research function. The reporting line into sales ensures accountability to pipeline, not to content volume.

Direct reports (to be hired):

• Market Research Analyst x2–3 — ICP & market intelligence, prospect intelligence, and competitive research.
• Data Science Engineer x1 — signal automation, AI pipeline, and CRM integration.
• Content & Enablement Analyst x1 (Phase 2) — outreach sequences, battlecards, and sales asset production.

Key interfaces:

• US Sales Lead — weekly sync; primary consumer of all intelligence outputs; commercial accountability partner.
• US Account Executives — end consumers of weekly package; source of deal context and feedback.
• Cloudbyz CEO — quarterly business review; white space and market gap intelligence feeds directly to product and strategy decisions.
• Cloudbyz Product team — white space research and competitive intelligence routed here to inform product roadmap.
• Salesforce / AWS Engineering team — coordination on CRM data model, API access, and shared infrastructure.

What kind of leader succeeds here

• Commercially wired: You have always cared about whether your work moved a number — pipeline, revenue, win rate. You are energized by accountability to commercial outcomes, not threatened by it. You have pushed back on stakeholders who wanted comprehensive research when a focused brief would have been faster and more useful.
• Builder mentality: You have built things from scratch before. You know what it feels like to be the first person in a function — the ambiguity, the lack of playbook, the need to make decisions without enough information. You made it work. You want to do it again with more experience and more resources.
• Bridge between execution and strategy: You can write a crisp account brief on Monday morning and present a market entry recommendation to the CEO on Friday afternoon. The best intelligence leaders operate at both levels simultaneously — they do not delegate all execution upward or all strategy downward.
• Comfortable with ambiguity: This role does not come with a playbook. The market segments, the signal library, the team structure, the toolstack — you will define all of them. Candidates who need clear scope before they start will struggle. Candidates who see a blank canvas and immediately start sketching will thrive.

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