Manager, Data Scientist

Overview

AI at Cvent: Leading the Future

Are you ready to shape the future of work at the intersection of human expertise and AI innovation? At Cvent, we’re committed to continuous learning and adaptation—AI isn’t just a tool for us, it’s part of our DNA. We’re looking for candidates who are eager to evolve alongside technology. If you love to experiment boldly, share your discoveries, and help define best practices for AI-augmented work, you’ll thrive here. Our team values professionals who thoughtfully integrate AI into their daily work, delivering exceptional results while relying on the human judgment and creativity that drive real innovation Throughout our interview process, you’ll have the chance to demonstrate how you use AI to learn, iterate, and amplify your impact. If you’re excited to be part of a team that’s leading the way in AI-powered collaboration, we’d love to meet you.

Disclaimer: Beware of Recruitment Scams – Legitimate Cvent recruiting communications will always come from an official ‘[email protected]’ 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.

In This Role, You Will

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 nto 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.
  
  

Experience

Here's What You Need:

• 10-13+ 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.