Retaining 1-person newbie service - guidelines?

budgetnow.ca is my side project. It is an express personal budgeting system at no cost. Yet, it has a modest and non-growing traffic. Somebody asked me if I retained a 1-person newbie service, how would I see it working. I write up my thoughts as a three step process:

1. Discovery exercise: (a) establishing current status of traction, (b) establishing where the gaps are
2. The fix: (a) high-level ways to plug the gaps, (b) detailed plan with acceptance criteria
3. Execution and delivery: (a) executing the plan, (b) hand over

I would certainly pay for 3 and 2b, but would be seeking a complementary 1. A goodwill 1 is needed to establish rapport and trust. 2a is up for negotiations. 

Top 10 questions for a potential startup employer?

Here is my list as a prospective employee. It is based on a Paul Graham's essay and my startup interviews earlier this year.

1. What is the value proposition? how do you validate it?
2. How often do you release the product to users? How do you interact with them and address their problems and comments?
3. How did the cofounders find each other? Are they all hands-on technical?
4. Are you pre revenue? break-even?
5. Who are your investors?
6. At your current burn rate, how many months of operation can you finance before next round of funding? 
7. What is the biggest risk to your startup?
8. Do you have an equity plan in place for the employees?
9. How are the working hours like?
10. Has anyone already left the company? If so, why didn’t it work out?

When everything is 4-star, how do you differentiate between them?

Ranking on a scale of 1 to 5 stars has emerged as a standard. Whether it's reviews on Amazon, Google or for an employer on a job board. The coloured stars seems to be an average of all the rankings. It certainly makes it easier for a reviewer to give a ranking from 1 to 5, essentially between a choice of 4. I like that. Imagine if one has to rank them from 0 to 100. I'll be thinking too much. Yuck!

The problem I find is that they concentrate around 4 stars. I propose to keep the coloured stars as-is but augment them with a percent (%) score.

How?

Anything less than half-a-star is hard to make out. This gives us 8 bins with width of 0.5 each, between 1 and 5. Assuming we round up all the decimal points, an average of 3.6 to 4 would render a full 4 coloured stars but the percentage score can will be between 72% to 80%. We get a bigger range if we add decimal points to % score.

Still not convinced. Continuing with our running example of two average boundaries that render full 4 stars, namely 3.6 and 4. 

percent score with 3.6 average = (review_count x 3.6)/(review_count x max_score(i.e.5)) x 100 = 72%. Similarly, it is 80% with an average boundary of 4.

Simple data science solving a real problem? :-)

Losing 15kg with (almost) the same eating habits

First, I have done it and produce the graph as evidence. I share this example because I found it “easier” than I thought and'd like to help my peers wanting to lose weight. It serves as a concrete example of “you optimize, what you measure” (source: unknown entrepreneur). I use this principle in my life on many fronts from work to budgeting. Anyways, back to healthy weight loss:

1. I measure weight every day on Fitbit’s Wifi smart scale, namely Aria. It keeps a log of my weight and shows my progress.
2. I culled processed sugar intake. They got it wrong. Sugar is the worst culprit.
3. I reduced food and carb intake by prepending every meal with raw salad. Salads can be tasty too.
4. For the afternoon snack, I eat simple popcorn (nothing fancy). They are voluminous and fill up tummy.
5. Big fan of green tea, specifically Genmaicha. It has a subtle taste, and I drink it hot (not boiling) with every meal instead of water.

Finally, I was consistent. Credits: @Sanaa Wasi for eating schedule, tasty salads and low-calorie snacks.

Don't ask Don't tell...this time current salary?

Recently visited NYC for a mini-reunion with school friends. One of them is a MD, one Research Professor, while the other two are bankers at Citi. Happy to know that banks are investing heavily in ML in both adoption and adeption. Though they don't see ML or valley as an existential threat, yet. I also see a lot buzz at the Canadian banks, hiring data scientists in masses. Many data scientists are PhDs with an interest or heavy weight academics. The unicorn in this case is someone who understands and marries business with machine learning.

One thing my friends were all excited about is that it is now illegal in NYC to ask about the salary history during the hiring process since October 31, 2017. I'm surprised that Ontario labour laws lag behind on this question. Not only employers and especially recruiters ask this question but the latter seem insistent on it. They also seem curious on the demographics of the applicant's residency. While the previous salary may definitely play a role in low balling, demographics seem more subtle in lowering salary expectations. My comment on this is top talent merits top dollars, or employers risk flight of talent.