Oleksandr Ruppelt

I write about data science stuff - my projects, my plans, my experience.

Building Data Science Consulting Practice

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0. The reason for this post

Recently I got an email with the job posting that got me interested. Now, it doesn’t happen very often, but when it does, I use it as a reality check - what I should have known by now to land this job if I wanted. The position was for a person that will build and lead the whole Data Science consultancy division in one big consultancy firm. You may know the name. Anyway, not sure if it is ethical to mention the name of the firm but also it doesn’t matter much.

This position I believed was an upgrade from what I do now (leading a service team) but I felt that the current circumstances (mass layoffs) in the tech industry are not favorable to change the place of work and also I am a great believer in DataRobot product. So I decided that instead I can imagine that I am hired to build the team from scratch alongside infrastructure around the team, hone the sales process and ensure that we are profitable, beating all the profitability benchmarks and earning love and money. This is how this blog emerged as a place to share my thoughts and experience but also theorize on the things I haven’t encountered just yet.

Leading the big team is overwhelming escpecially if you are hired from the outside. If you are a home-grown promoted manager you should be aware of internal politics, short pathways and possible pitfalls. At the same time, not only the team matters but the way you are doing business - how you find your clients, win them over, deliver the use case and make your client succesful. If you are working in a product company - you need to treat your stakeholders as clients so there will be small difference from consultancy. Here I’ve listed all the stuff a Data Science Manager need to ba aware of or deal with and will be covering them in more details later

1. Your team

Your manager (managing up)

New to the company or not - communication with your direct manager oftentimes is your first priority.

Team composition
Your allies, your friends, your opposition

Knowledge is the source of the power. And

2. Your Data Science Practice

Quick iterations (AutoML)
Your codebase
Your infrastructure (security, data storage)

3. Your Sales

How to work with your Sales team.
Pitch decks and their importance
Packaging your offer
Pricing

Your pre-sales

Difference from usual IT development

Why need POV
What requirements of succesful POV
Bake off or no?

4. Your Delivery

The SOW
Delivery of what is promised
Project creep

5. Support

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