Data Science

The Data Science section is focused on leveraging ML and AI in the GitLab product and preventing abuse in the application.

Vision

Build a diverse and global development team to support and drive results across the section, while maintaining our values and unique way of working.

Mission

Drive results through iterative development as we add AI, ModelOps and Anti-abuse features into the product. Our teams are data-driven, support dogfooding, and collaboration within GitLab and the wider community.

Stages

Career Development

Giving / receiving feedback

It can be hard to understand how you’re doing in your role, because feedback can come off as formal (annual reviews, 360 surveys, career development conversations, goal check-ins) or casual (in Slack channels, 1-1’s, MR reviews, team meetings.) We receive various kinds of feedback regularly and through different formats, so the type of feedback you’re receiving is not always clear. In order to be more intentional about the types of feedback given, here is a classification chart based on three types of feedback:

Label Meaning Example
(appreciation) I want to thank you for doing this, and please do more of it in the future “I did not expect that you would have created a working group, because you’ve done so, our whole team will benefit from the results.”
(coaching) I’m trying to help you improve a behavior you are already exhibiting or change a behavior that you currently have “The reports that you give me are very helpful, and in the future we can schedule them for the first of the month to be more consistent.”
(evaluation) Tells you where you stand according to existing standards or expectations “My expectation was that our decision would be transparent. Since it was not, our team has forgotten the decision, so we must be sure and meet that expectation next time.”

Engineering Managers

This section lists relevant experience areas for individual contributors interested in the management track, new engineering managers, or existing engineering managers who may be lacking opportunities. This list can be used to identify opportunities in these areas.

Expert hiring manager

Expert hiring manager

  • Experience with behavioral interviews
  • Screening candidates for your team
  • Identifying cultural answers or clarifying vague answers
  • Identifying a headcount need in advance
Performance management

Performance management

  • Crucial conversations
  • Performance improvement plans
  • Coaching on improvement areas
  • Giving feedback
  • Identifying underperformance
Communicating company decisions

Communicating company decisions

  • Annual review, calibration sessions, compensation discussions
  • Motivating team members on opportunities that come with negatives (borrow requests, engineering allocations, feature change locks)
Product area

Product area

  • Triage reports
  • Define and monitor productivity metrics, take action if necessary
  • Collaborative planning
  • Proactively identifying issues or recommending engineering allocations
  • Leading an incident in your area
  • Proposing and driving a borrow request (reactive)
  • Shared OKRs and delivery
Team success

Team success

  • Career growth development leading to promotions, mentors, technical interviews, maintainers
  • Setting goals based on 360 feedback and career aspirations
  • Smooth onboarding process
  • Frequent and transparent handbook updates
  • Identifying performance indicators for the team
  • Becoming a mentor
Achieving consensus

Achieving consensus

  • Facilitating a working group
  • Participating in stage, sub-department, skip level, and/or engineering manager discussions
  • Coordinating the dev on-call
  • Experience with being Incident Manager On-Call (IMOC)
  • Collaboration with the full product group quad planning
Personal growth

Personal growth

  • Receiving feedback
  • Continued learning, identifying new growth opportunities, and building a personal growth plan
  • Seeking a mentor

Trainings offered by GitLab for EMs

Other resources

Staff Engineers

For an explanation on what to expect as a Staff engineer and a list of ideas for tactical initaitives, visit this page.

Team Days

Engineering management schedules Team Days every 2 quarters as a way to celebrate our wins as a team, with our counterparts, and look forward to the future. These are optional social events led primarily by the attendees in terms of activities and discussions, but scheduled and organized by engineering managers.

Sync meetings will occur in all time zones, and activities may happen throughout the day in a dedicated team day Slack channel. While optional, all team members should have an opportunity to participate for as long as they want, so capacity for all engineers should be accounted for during milestone planning.

Say/Do Ratio

When we commit to a new milestone, engineering managers apply the ~Deliverable label to all issues they are able to commit to based on capacity. This serves as a promise from engineering to product as well as a signal for the most important issues in the milestone for the team to pick up. At the end of the milestone, successfully completed issues with the ~Deliverable label are assessed against the total number of ~Deliverable issues in order to create the Say/Do Ratio (7 issues completed of 10 committed to is a 70% Say/Do result.)

The Say/Do Ratio is a metric used to:

  • Provide predictability and stability when milestone planning
  • Raise flags about the types of issues that are frequently disruptive
  • Measure how well we are meeting our commitments to our customers

Meetings

Although we have a bias for asynchronous communication, synchronous meetings are necessary and should adhere to our communication guidelines. Some regular meetings that take place in Manage are:

Frequency Meeting DRI Possible topics
Every other Thursday Engineering managers discussion @m_gill Ideas, help or resources needed from others, concerns, questions, etc.
AI-Powered Stage
The AI-Powered Stage in the Data Science section is focused on providing applied AI capabilties to the GitLab product.
ModelOps
The ModelOps stage aims to empower GitLab customers to build and integrate data science workloads within GitLab.
Last modified August 27, 2024: Update Foundations stage handbook and redirect Manage (88b27eb9)
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