Managing Data Science Teams @ DataNights

DataNightsInstructor22'

The first DataNights Data Science Management cohort focused on the practical realities of leading data science teams, from project roadmaps and product work to recruiting, feedback, and peer review.

The first cohort ran from August 3, 2022 through September 14, 2022 in weekly evening sessions, hosted by Forter in Tel Aviv.

Organizing Team

Shay Palachy Affek - Program lead and instructor, DataNights / Datahack
DataNights / Datahack volunteer organizing team - Cohort operations and community support

Lectures

5 Things I Learned as a DS Manager - Ilana Klovatch Lessons on methodology, team strengths, and leading people with different skill profiles.
Make It Worth: How to Successfully Deliver ML to Production - Inbal Budowski-Tal A practical talk about the production pitfalls that keep ML projects from delivering real business impact.
Data Science Project Workflow + DS Playbooks for DS Teams - Shay Palachy Affek Frameworks and workflow patterns for structuring data science projects and team practices.
Data Science and Product Management: Function or Dysfunction, There Is No Try - Adina Lederhendler A talk about how data science leaders can work effectively with product managers despite uncertainty and technical translation gaps.
Objective-KPI Alignment in Data Science Projects - Shay Palachy Affek A discussion of how technical objectives in data science work align or misalign with business and product KPIs.
Building a Data Science Team: How to Get the Right People and Make Them Work Together Efficiently - Nurit Vatnik On selecting from diverse backgrounds and shaping a team that collaborates well.
Interview Templates for Different Seniority Levels of Data Scientist - Shay Palachy Affek A practical framework for adapting technical data science interviews to different seniority levels.
DS Archetypes & DS Tech Interviews - The cohort A hands-on group activity that turned the archetype and interview frameworks into concrete hiring changes.
The Imposter Syndrome: Managing Senior Data Scientists, and Keeping Up with Team Knowledge When You Are Not Hands-On - Adi Nesher A talk about leading experienced researchers and senior practitioners without needing to outsmart them.
The Right Project Mixture: Maintaining What’s Working While Delivering Innovation - Amit Weil How data science leaders balance model maintenance responsibilities with delivering novel work.
Data Science, Validated: Testing & Monitoring ML Projects - Shir Chorev Model monitoring, failure detection, and how to keep production ML systems healthy over time.
The Secret Sauce of Data Science Management - Shir Meir-Lador Lessons on building teams that thrive while still delivering winning results for the organization.
Peer Review in Data Science Projects - Shay Palachy Affek A talk about adapting peer-review practices to data science work to improve decision making and output quality.
Participant takeaways - The cohort A closing session for participants to share what they had already applied from the program.
Lecture Workshop - The cohort A collaborative workshop on ideas and possible additions for future cohorts.

Course Outline

Course Syllabus