Were seeking an experienced and visionary DataOps Team Lead to manage our extensive team responsible for data operations and services. As the DataOps Team Lead, you will play a pivotal role in enabling critical deliveries to our clients through data quality assurance, data tagging and labeling, data analytics, and more.
Responsibilities:
Team Leadership & Resource Management: Manage the work of 4-7 diverse DataOps personnel (in both a local and a second global site), including annotators and analysts, outsourced work from external companies, team timelines, resource allocation, run the team sprints, and work with stakeholders to understand needs and secure both contributions to research projects and successful deliveries to clients.
Business Understanding: Maintain team alignment on company goals, and ensure team operations and deliveries meet the business requirements.
Data Quality Assurance Responsibility: Oversee (and be accountable for) the work of our DQA Lead, which ensures the quality of all customer facing data and deliverables, company wide. Drive execution on scale, using processes and tools instated for the purpose.
Process understanding and characterization: Define new manual, analytical and technical DataOps-owned components and services to answer (in-house) customer needs. Also, alter or expand existing services/components (one-time or permanently)
Develop & Implement New Tools and Infra: Define new systems and features required by your team, and work with engineers and data scientists to lead their implementation and adoption. Drive use of analytical tools to improve work processes and the quality of deliverables.
Employee development: Grow, challenge and mentor a team of diverse individuals.
Responsibilities:
Team Leadership & Resource Management: Manage the work of 4-7 diverse DataOps personnel (in both a local and a second global site), including annotators and analysts, outsourced work from external companies, team timelines, resource allocation, run the team sprints, and work with stakeholders to understand needs and secure both contributions to research projects and successful deliveries to clients.
Business Understanding: Maintain team alignment on company goals, and ensure team operations and deliveries meet the business requirements.
Data Quality Assurance Responsibility: Oversee (and be accountable for) the work of our DQA Lead, which ensures the quality of all customer facing data and deliverables, company wide. Drive execution on scale, using processes and tools instated for the purpose.
Process understanding and characterization: Define new manual, analytical and technical DataOps-owned components and services to answer (in-house) customer needs. Also, alter or expand existing services/components (one-time or permanently)
Develop & Implement New Tools and Infra: Define new systems and features required by your team, and work with engineers and data scientists to lead their implementation and adoption. Drive use of analytical tools to improve work processes and the quality of deliverables.
Employee development: Grow, challenge and mentor a team of diverse individuals.
Requirements:
At least 3 years managing a team (ops, analytics, deliveries, QA, etc.).
Advantage: Managing teams composed of diverse roles and disciplines.
At least 3 years in data/analytics-centric operational role (e.g. delivery/implementation, technical sales, support/operations of data-centric products).
Experience with improving or overhauling data or delivery processes, tools and methodologies.
Advantages:
Experience working with or in a data science team.
Python (at least a good degree of familiarity and some practical experience).
Basic knowledge of SQL.
A science, technology, engineering or mathematics degree.
At least 3 years managing a team (ops, analytics, deliveries, QA, etc.).
Advantage: Managing teams composed of diverse roles and disciplines.
At least 3 years in data/analytics-centric operational role (e.g. delivery/implementation, technical sales, support/operations of data-centric products).
Experience with improving or overhauling data or delivery processes, tools and methodologies.
Advantages:
Experience working with or in a data science team.
Python (at least a good degree of familiarity and some practical experience).
Basic knowledge of SQL.
A science, technology, engineering or mathematics degree.
This position is open to all candidates.
