Thank you for subscribing and for all your feedback this week. I'm building two posts this week based on a subscriber's questions. The first will be an introduction to expert systems, and the second will be an introduction to dynamic pricing. I appreciate your questions because they guide me to gaps in my content.

Last week, I added several posts and decided to avoid spamming your inboxes.

The Value Of Experience With Maintaining And Improving Models For Junior Data Scientists

The Benefits People Outside Our Field Bring When They Transition Into Data Science

My Data Science Hiring Mindset And How I Got It

How I Quickly Screen Data Science Resumes

A Simple Framework To Keep Data Science Projects Aligned With Business Value

Using Micro Engagements To Launch A Data Science Business

Quick Tips To Improve Data Science Job Description Quality

Years Of Experience Is A Terrible Hiring Filter

Why Causal Is The Future Of Machine Learning

Career Opportunities For Data Scientists In Business Turn Arounds

My Thoughts On Open Sourcing Twitter's Algorithm

Nike, Machine Learning, NFTs, and the Metaverse

My Updated Data Science Learning Path With Basic Learning Resources

This post is for subscribers only, and I will be filling in details for each role in subsequent posts. This one will contain links to all the deep dives.

My List Of Data Science Roles

I added a new service this week. If your company's hiring process is a barrier that pushes the best people away, send your HR or TA team my way. I know the frustration firsthand, and it isn't easy to fix from the inside. HR teams are more receptive to outside consultants than internal feedback for whatever reason.

I've built out Data and Analytics organizations for clients for over seven years. I needed to overhaul the hiring process at each business because it was a brick wall to attract the talent they needed to succeed. I am offering one and two-hour micro advisory sessions to help companies transition from legacy to modern hiring processes.

I have added new lessons to Business Strategy for Data Scientists. As subscribers, you get 15% off the introductory price using the code SUBSTACK15 for the next week. Please don't pay full price. Next week, I will release new lessons and bump the price up. Subscribers will always have access to the course at the introductory rate, and I'll send out a new code next Monday.

I am wrapping up a new course on starting a business in the data space. My micro engagements to advise people interested in launching their first business have taken off over the last three months. Most courses are built for people building a unicorn startup. There's almost nothing available for the 90% of us who are looking for a more pragmatic approach. Everyone I've worked with is focused on building and growing six to seven-figure annual revenues, not ten.

Over the last month, there's been a shift in thinking about startups and services businesses. I've heard it in the language VCs use and the sentiment of people who put their money in VC funds and SPACs. VCs are hungry for high-quality deal flow. I get messages every month asking if I'm working with startups that fit a new paradigm.

Investors are looking for engaged, growing communities built around the startup's products. They want paying customers and realistic strategies for growth. Their startup evaluation frameworks use modern KPIs to evaluate fundamentals like margins, pricing power, and revenue growth. Valuations and initial investments are starting to make sense after the last seven years of insanity.

This cycle is being driven by a wave of experienced, pragmatic founders. Service and product-focused founders create companies with substance over slide deck strategies. Most aren't interested in VC funding and are bootstrapping instead. Side hustles turn into businesses once they create revenue streams that exceed their current salaries.

No one's jumping out of an airplane and building their parachute on the way down anymore. Small businesses in the data industry are launching with $10K-$25K in monthly recurring revenues and growing to $100K-$300K in MRR in 2-3 years.

Founders succeed by being hyper-focused on returning high value to a narrow customer segment. It's a smaller market size which creates a ceiling for growth, but that's still a very comfortable living for founders. It's more than they'd get putting the same effort into working for someone else.

Businesses are evaluating their data science teams with a newfound pragmatism too. Companies are looking for leaders who can move data science teams from a cost center to a cost saver to a revenue generator. My potential clients for the second half of this year are interested in rebalancing their teams and swapping out leadership. Senior leaders expect people on the data science team to play a more significant role in growing their business.

Several years ago, I got offers from Amazon, Facebook, IBM, and SAP to come in as an employee and put the work into building their businesses. Many data scientists are faced with the same decision I was, put the effort into someone else's business or my own. A growing number of people are exploring their own businesses.

That's our new normal. This new wave of founders is the next generation of CEOs. Some will scale their businesses beyond their initial markets and approach unicorn values through long-term growth. Others will be bought out over the next five years by larger businesses looking for growth opportunities. It will be easier to buy growth from successful small businesses than build it in the current economic conditions. Founders will be promoted into big businesses' C Suites through this route.

The next two years will be filled with change. It doesn't take a visionary to make that call. Change creates opportunities for entrepreneurs, leaders, and strategists. I've spent the last several months writing about the opportunities I see and how to position yourself to take advantage of them. Fortune favors the prepared mind in times of change.

Vin

High ROI Data Science

A Monday Full Of Changes