Director of Engineering, AI & ML, Data Collections

At PitchBook, a reputed company company, we are always looking forward. We continue to innovate, evolve, and invest in ourselves to bring out the best in everyone. Were deeply collaborative and thrive on the excitement, reputed company, and fun that reverberates throughout the company.

Our extensive learning programs and mentorship opportunities help us create a culture of curiosity that pushes us to always find new solutions and better ways of doing things. The combination of a rapidly evolving industry and our high ambitions means theres going to be some ambiguity along the way, but we excel when we challenge ourselves. Were willing to take risks, fail fast, and do it all over again in the pursuit of excellence.

If you have a good attitude and are willing to roll up your sleeves to get things done, PitchBook is the place for you.

About the Role:

The Data Collection AI/ML team sits at the intersection of automation, AI, and data quality. The teams mission is to accelerate and scale PitchBooks data coverage by applying advanced ML models to identify, extract, and validate entities, relationships, and key insights from vast collections of structured and unstructured sources, including filings, PDFs, and market documents.

As the Director of the Data Collection AI/ML team, you will partner closely with Data Operations, Data Collections Product and Engineering teams to deliver seamless, intelligent data pipelines that transform raw inputs into high-quality, actionable data.

As the Director of Data Collection AI & ML you will lead the strategy, vision, and execution of AI and ML initiatives focused on automating PitchBooks data extraction, enrichment, and validation workflows. Your organization will be responsible for building intelligent systems that ensure PitchBooks data collection is as accurate, comprehensive, and timely as possible, leveraging cutting-edge document AI, OCR, and entity resolution techniques applied to financial documents and other proprietary data sources.

In this highly visible leadership role, you will manage a global team of 15+ data scientists and machine learning engineers, driving both innovation and operational excellence. You will define the roadmap for automation and enrichment across multiple data domains and ensure scalable, production-grade AI systems that directly power PitchBooks data ingestion pipelines and platform accuracy.

Your leadership will guide the design and deployment of AI-driven extraction and enrichment models, including document classification, named entity recognition, relationship extraction, and quality validation systems. You will also play a key role in shaping cross-functional data and AI strategy in collaboration with Product, Data Operations, and the client-facing Insights AI/ML teams, ensuring consistency, reliability, and alignment with enterprise data objectives.

This role demands a visionary yet hands-on leader one who can balance strategic direction with technical credibility, and who can influence across teams and executive levels while ensuring the continued professional development of a globally distributed technical team.

In addition to driving product impact, this role offers an opportunity to shape PitchBooks growing presence and technical reputation in the AI and ML space. We are looking for individuals who are active contributors to the broader AI community through peer-reviewed research, technical publications, or open-source initiatives. Candidates who have authored conference papers or patents and who are excited to explore the frontiers of generative AI, LLMs, and applied NLP will be well-positioned to help us both advance our internal capabilities and deepen trust with our customers through thought leadership.

Primary Job Responsibilities:

Define and execute the AI & ML strategy for data collection, extraction, and enrichment automation aligned with PitchBooks long-term data strategy

Partner with senior leadership to identify high-impact opportunities for AI-driven automation and cost reduction in data collection workflows

Establish success metrics and operational KPIs for automation accuracy, throughput, and coverage improvement

Lead, hire, and develop a high-performing global team of data scientists and ML engineers; define team structure, roles, and growth paths that align with organizational goals

Foster a culture of innovation, accountability, inclusion, and continuous improvement across distributed offices

Champion hiring, mentorship, and professional development initiatives to grow internal AI/ML talent

Elevate engineering excellence through code reviews, design reviews, and technical guidance for ML engineers and scientists

Act as a reputed company by shaping best practices for experimentation, model evaluation, responsible AI, and scalable ML engineering

Guide teams across the organization toward cohesive, reusable, and standards-aligned architectures

Collaborate closely with Engineer