About This Role
The key technical requirements and skills for the Data Analyst Lead position at Pyramid Global Technologies include expertise in Python, R, SQL, shell scripting, GitHub, CI/CD tools like Teamcity, and monitoring tools like Datadog. The role also requires familiarity with cloud computing environments, particularly AWS. The team and project information indicate that the focus is on advancing the automation, testing, quality control, observability, and proactive maintenance of the company's data science portfolio. The lead will work with a team of data science operations specialists and collaborate with an infrastructure platform team. This role is notable for its emphasis on automation, reliability, and quality of service for data science products. The candidate is expected to have experience in managing sprints and product backlogs in an Agile environment. The job posting does not mention any specific salary or benefits information.
An Analytics Platform Lead is required to lead a team of highly motivated and capable Data Science Operations specialists to advance the platform and processes used to develop, deploy, monitor, track performance and quality, update/maintain Data Science existing and new analytics and data science models. Working with infrastructure platform team, focus is on advancing automation, testing, quality control, observability and proactive of the Data Science portfolio.
Required Technical Skill Set: Python/R/SQL, shell scripting, GitHub (version control), Teamcity or other (CI/CD tools), monitoring tools such as Datadog, awareness of cloud computing environments (AWS).
Required Ability to work as part of a distributed and multidisciplinary team and develop and maintain relationships with internal and external stakeholders.
Required Assist in the management of sprints and product backlog in an Agile environment.
Required Focus of team is automation, reliability and quality of service for Data Science products.
Desirable Application of advanced analytics, machine learning, artificial intelligence, and/or optimization algorithms. Understanding of data science fundamentals, methods and techniques.
Desirable Comfortable with analytics and data science project life cycle: scoping, building, testing, implementation, maintenance, tracking and quality assessment of analytics and models."
Originally posted on Himalayas