UTM.io
Website:
utm.io
Job details:
Reports to Head of Data Science & AI in Cvent Analytics Organization
OUR CULTURE AND IMPACT
Cvent is a leading meetings, events, and hospitality technology provider with 5,000+ employees
and 24,000+ customers worldwide, including 60% of the Fortune 500. Our technology powers
millions of events annually — transforming how people connect through the world's most
innovative event management platform. Our culture rewards intrapreneurship: every Cventer is
expected to think and act like a founder, embrace risk, and make decisions that move the business
forward.
AI AT CVENT
At Cvent, AI is central to how we work — not a project layer on top of it. We value professionals who integrate AI thoughtfully into their daily practice, combining technical rigour with human judgment to drive real innovation. If you experiment boldly, build with care, and want to help define what AI- augmented work looks like at scale, you will thrive here.
Disclaimer: Beware of Recruitment Scams – Legitimate Cvent recruiting communications will always come from an official ‘name@cvent.com’ email. We never request any payments or ask for sensitive personal or financial information via chat or social media platforms. For more information, please visit: https://www.cvent.com/en/notice-recruitment-fraud
THE OPPORTUNITY
The Analytics team is Cvent's internal intelligence engine — the function that powers how the
business understands performance, serves commercial teams, and accelerates decision-making
with data and AI. Within this organization, the AI Engineering & Data Science team designs and
deploys production-grade AI and ML solutions with a primary mandate across commercial and
growth teams - Sales, Marketing, Revenue, Growth teams and others.
This role is designed for an experienced practitioner ready to step into a leadership position
alongside the Head of Data Science & AI. As Manager, you will own delivery, people, and technical
standards while remaining a hands-on contributor who can architect and build alongside your
team. You are not being hired to manage from a distance — you are being hired to raise the floor
and the ceiling of what the team can do.
The right person for this role brings commercial instinct alongside technical depth. You understand
how commercial teams operate, can translate sales and marketing problems into AI solutions, and
know how to take work reliably from prototype to production. You are also a developer of people:
you build individual capability, hold high standards, and create an environment where engineers
grow.
WHAT YOU WILL DO
AI & Data Science Function
- Partner with the Head of Data Science & AI to run the team — owning delivery execution, team health, stakeholder relationships, and technical direction on a day-to-day basis.
- Serve as the senior technical expert and partner with Architects to set architectural direction, lead design reviews, and make build-vs-buy decisions that shape how solutions are structured and scaled.
- Represent the team in cross-functional forums — with commercial and technical teams translating AI capability into business language and AI business needs into engineering scope.
Own End-to-End AI Delivery
- Lead delivery across the full AI and ML lifecycle — from problem framing and data acquisition through model development, evaluation, deployment, and post-production monitoring.
- Set and enforce engineering standards: experiment tracking, model versioning, CI/CD for ML pipelines, observability, reproducibility, and documentation. Build a culture that ships with both quality and speed.
- Design and implement agentic AI workflows — LLM orchestration, retrieval-augmented generation (RAG), natural language querying (NLQ), multi-step reasoning pipelines, and human-in-the-loop architectures — across internal commercial and operational use cases.
- Own the team's AI observability posture: output quality monitoring, drift detection, evaluation frameworks, and feedback loops that keep production systems reliable, explainable, and auditable.
Drive AI Use Cases
- Partner closely with cross-functional stakeholders to identify, prioritize, and deliver AI solutions that improve pipeline quality, conversion rates, and revenue predictability.
- Lead development of Sales and Marketing AI applications — lead scoring, next best action, intent signal modelling, account health prediction, churn early warning, and conversational analytics.
- Translate commercial problems into well-scoped AI initiatives with defined success metrics, baseline comparisons, and measurable business outcomes. Own the narrative when presenting to commercial teams’ leadership.
- Proactively surface new AI use cases with business partners — qualify opportunities by strategic impact and technical feasibility and build the internal case before pursuing them.
Build and Develop the Team
- Directly manage a team of data scientists and AI engineers — owning hiring, onboarding, performance management, and individual development plans.
- Run structured 1:1s focused on technical growth and career progression. Maintain a skills matrix and design development plans that close capability gaps systematically.
- Foster a high-performance delivery culture: rigorous code review, clear working rhythms, cross-functional stakeholder accountability, and a bias toward shipping working solutions over perfect prototypes.
Shape AI Platform and Architecture
- Work with Data Engineering and the Head of Data Science & AI to evolve the team's tooling stack — model registry, feature pipelines, LLM orchestration layer (LangChain, LangSmith, or equivalent), and evaluation infrastructure.
- Contribute to Snowflake as Cvent's enterprise intelligence layer — ensuring ML feature pipelines, semantic layers, and AI workloads are architected for reliability, reuse, and long- term auditability.
- Embed responsible AI practices into delivery: privacy assessments, governance reviews, and alignment with Cvent's AI usage policies throughout the build lifecycle.
WHAT YOU BRING
Experience
- 8–10+ years in data science or AI/ML engineering, with a minimum of 2 years in a people management or formal technical lead role with clear delivery ownership.
- Demonstrated track record delivering AI and ML solutions for commercial team — lead scoring, churn prediction, intent modelling, revenue forecasting, NLQ, or comparable commercial applications — in a SaaS or B2B environment.
- Experience working with Salesforce, Marketo, or equivalent GTM platforms as primary data sources. Familiarity with how commercial data flows from CRM to analytics is essential.
Technical Depth
- Strong AI engineering fundamentals: LLM application development, RAG and NLQ architectures, agentic frameworks (LangChain, LangGraph, LlamaIndex, or equivalent), prompt engineering, and model evaluation at production scale.
- Hands-on ML expertise across the full lifecycle: feature engineering, model training and selection, A/B and champion-challenger testing, deployment, monitoring, and drift management in production.
- Proficiency in Python and SQL. Hands-on experience with Databricks (MLflow, Delta Lake, Jobs) and Snowflake (Cortex, ML functions, feature pipelines) as the core delivery stack.
- Experience with vector databases and semantic search infrastructure — OpenSearch, Pinecone, Weaviate, ChromaDB, or equivalent — for embedding-based retrieval in RAG and NLQ systems.
- Hands-on ability to build internal AI applications and data tools using Streamlit, React, or Node.js — from early prototype to production-ready product.
- Familiarity with AI observability tooling (LangSmith, Langfuse, Datadog, or equivalent) and ML experiment tracking (MLflow, Weights & Biases).
Leadership and Collaboration
- Proven ability to co-lead a team — setting direction, holding standards, developing people, and creating delivery accountability without micromanaging execution.
- Fluency working across Data Engineering, BI, and business teams — translating between technical model requirements and downstream pipeline, reporting, and stakeholder workflow needs.
- Strong commercial communication: the ability to frame AI solutions in terms of business outcomes, present findings, and defend modelling decisions under scrutiny.
- A hunter's instinct: proactively identifying where AI creates leverage, building internal business cases, and expanding the team's scope through demonstrated impact — not just by executing assigned work.
Click on Apply to know more.