Data Ops Lead

New York

Data Engineering

Permanent

Our client, an innovative AI and Data company, is hiring a Data Operations Lead to join the team in New York. The successful candidate will own the end-to-end data lifecycle, shaping data operations, developing scalable processes, managing external partners and ensuring the highest standards of dataset quality to deliver high-quality, production-ready datasets for leading AI research teams.

Responsibilities

Own the end-to-end delivery of high-quality AI training datasets, from raw audio data collection through to production-ready outputs.
Collaborate with AI research teams and external partners to define, manage and deliver dataset requirements.
Support the development and execution of commercial data agreements, ensuring successful delivery against customer expectations.
Design, build and optimize operational workflows across transcription, annotation, quality assurance and dataset production.
Develop robust data quality frameworks, acceptance criteria and validation processes to maintain delivery standards.
Lead outsourced and international data operations teams, ensuring quality, efficiency and timely delivery.
Partner with engineering teams to enhance data pipelines, tooling and operational scalability.
Identify and implement improvements to increase dataset quality, reliability and operational efficiency.
Serve as a key liaison between customers, internal stakeholders, engineering teams and external vendors.
Contribute to the company’s data strategy and help scale data operations as the organization grows.

Skillset

Minimum of 5 years of experience building and scaling data operations, data pipelines or AI/ML data workflows.
Proven experience working with audio, speech or multimodal datasets within AI or machine learning environments.
Strong track record managing external data partnerships, dataset delivery and AI training data programs.
Experience leading global or outsourced teams focused on data annotation, transcription and quality assurance.
Strong understanding of data quality frameworks, validation processes and operational performance metrics.
Technical proficiency across data pipelines, cloud infrastructure and AI/ML workflows.
Knowledge of digital audio fundamentals, including sample rates, voice activity detection (VAD) and audio processing formats.
Experience developing or managing AI training datasets for applications such as ASR, TTS, speaker identification or conversational AI.
Familiarity with audio processing and data tools such as Librosa, FFmpeg, SoX, torchaudio, and cloud platforms including AWS, GCP or Azure.
Entrepreneurial mindset with the ability to thrive in a fast-paced environment, take ownership and make decisions with limited information.