Signs of an Effective Data Science Manager - PowerPoint PPT Presentation

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Signs of an Effective Data Science Manager


The data science manager is responsible for helping the organization leverage data, working with and through a team of data scientists and engineers to provide valuable direction and insight for management to make informed decisions.I think there are 5 qualities of a highly effective data science manager: Balances technical nuances across domains of data, math/stats, machine learning and software and connects them to business context and value. Want to take a data science course? Learnbay offers the best data science course in Pune. Students work on practical assignments created by professionals in the field. – PowerPoint PPT presentation

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Date added: 6 July 2022
Slides: 9
Provided by: keerthi2301


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Title: Signs of an Effective Data Science Manager

Signs of an Effective Data Science Manager
We all must have heard of the push and
pull model at some point in our professional
life. One analogy to this push- pull concept is
whether you keep waiting for somebody to push th
e work to you. you are opportunistic enough
to understand the business problems and
pull the high-impact work on your plate.
Meeting the Teams Expectations
There is a perception that a data science team
gets to work on and with new state-of-the-art
cutting-edge models all day long. But the
reality also includes building automation
workflows for repeat tasks, working on a proof
of concept for 4 months only to see it scraped
off, model monitoring, and maintenance,
explaining model predictions, etc. to name a few.
Keeping up the Teams Morale
That the management understands and plans an
individuals development and growth charter
where business objectives converge with the
teams goals and interests. But presently we
need to finish a business deliverable and
whenever the next opportunity arises, the teams
alignment will be worked out as raised by them
in one-on-ones.
What do You Promote Competition or Individual
A good data science manager holds himself
accountable for the teams learning curve and
promotes some buffer time to explore the work of
their own interest. That's where the good part of
management shines through - you motivate them to
spend 15-20 of their time exploring the new
algorithm, learning a new skill like a
programming language, etc.
Business vs ML Metrics
Your ability to make your stakeholders ML-aware
will take you farther in this career. It shows
that you are able to think long-term and are
able to bridge the gap between churning models
with high accuracy vs building the models that
add tangible business value. The resource
expensive and needs a clear understanding of the
opportunity size and impact of ML models.
we all wish for a rulebook that can set all
managers to success from the first day,
unfortunately, there isnt one. Everyone has
their own management style and should stick to
what comes effortlessly to them. You can not
fake yourself long when managing teams. So, it's
best to be your genuine self and do what an
effective manager does - delivering the business
outcomes while keeping the team motivated.
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