Author: Fan

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Why we need women in leadership team?

I know the typical answer is “Diversity & Inclusion”. I just want to share a real example to reflect that.

I am having regular coaching sessions with a few juniors in my team. In one of the conversations, I noticed male team members are in general more dominating in the conversations (as expected), and I am not saying it is not allowed or bad to drive. However, as a result, there is always some hesitation for females to share their views. Under this situation, a woman who is in the leadership team would balance the “energy” and make the conversations more encouraging for female colleagues to share their thoughts.

When the team members are asked what their strengths, you often hear ambitious / dare from male colleagues, and empathetic / approachable from female colleagues. We do need to be competitive and ambitious but at the same time, there is never enough empathy.

We need to hear views from both male and females.

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How to have the “leaving” conversation gracefully and affirmatively

I am sure many of us have or will change jobs, and many of us then will have conversations with our managers or HR, who would like to retain us. First of all, congratulations! It takes much more courage to leave your current position than stay. Secondly, being invited to have this type of conversations means you are an valuable asset to your current employer. It is a recognition of your past achievements. So let’s face them positively.

But, at the same time, I fully understand the conversations are not easy. It needs convincing, needs certain level of openness, needs to be affirmative, and needs to be handled gracefully. 

Here I am going to share a few tips with you:

Start from appreciation—

Our employer provides the platform for us to perform; People around us support us in one way or another in our achievements; Now they spent time and efforts to understand what they can do to retain us. So, we are grateful for that. 

Maintain positive tone —

I am sure there are some “frustrating” moments that pushed you to quit your job. We also often hear people are saying “9x% of people quit their job because of their managers.”. Or you may feel you are under utilised. Or you might think you are not given enough opportunities or exposures. There are various reasons for us to make the decision. No matter of what, try to phrase them in a positive way. I am not saying we need to lie. It is not lying. It is also a psychological signal to ourselves by phrasing it positively— we are simply looking for a better opportunity for us to excel, to discover the potentials within ourselves, to adventure into a new role, to explore a new environment. After all, we are looking for a better way for us to succeed. A good example could be, you are moving from a MNC to a startup, because you might feel your career has been stagnant and you want to have a “promotion”. Then you can just tell HR or your manager that you are trying to explore a more dynamic environment and take up more responsibilities.

State the reasons of leaving affirmatively and confidently —

Decision of leaving is never easy. You must have thought over it, even have had a few sleepless nights to weigh different options. Although you have finally decided to leave, any kind of “retention” conversations may make you hesitate again. If the end goal is to leave, we should make our tone affirmative. It is our life and our career, and we are the CEO of the company called “myself”, so we are making the decisions. If we show any sign of hesitation, it will only make the conversation even longer and more unproductive for both sides.

Ensure the management that your team are strong enough to continue forward — 

Our current employer is concerned over our leaving because they see the value in us. At the same time, they are also concerned about the responsibilities we carry, the projects we are working on, the team we are leading, or the clients we are managing. We need to tell and ensure the management that all of the concerns above are well taken care of. You have a strong team who will continue the delivery, you probably even have the candidate as your successor.  So nothing to worry.

Get rid of the “guilt” feeling — 

I know you must feel guilt for something, maybe to your team that you thought you leave behind, to the newly hired person who needs to say farewell even before he / she passes their probation, or to the organization that has provided so many opportunities to help you grow, or to the colleagues who you like to work with, or to the managers who trust you and you trust. Human beings have emotions. We are often attached to what we do and who we work with. Especially those attachment and people used to motivate us. However, any change will bring new opportunities for other colleagues. Now because you leave, there is an opening so other people may get promotion. Sometimes, your leaving may trigger some changes in the strategy or directions. So in the end, it is good thing for other people. We shouldn’t feel guilt. 

Hope the tips are helpful.

And more importantly, hope you continue your success in your next role. 

“Any ending is a new beginning!”

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What do I do after working hours as data scientist?

When we prepare for interview, very often we see one of the requirements is “be familiar with statistics and mathematics”. We are required to know Statistics because it is really the fundamental part of data science. So in order to keep Statistics knowledge fresh all the time, what do we need to do?

One thing I have been doing is to refresh my memory on Statistics all the time. Recently what I picked up is the Machine Learning Flashcards:

https://machinelearningflashcards.com

I found it extremely useful and in fact also a lot of fun to learn them. Thanks for Chris Albon, the author of the 300 flash cards. He brought a lot of fun into my learning.

How I learn / re-learn them?

  1. I tried it for three days, spent minimum one hour every day, mainly to test how many I can complete every day. And then set a target of >= 6 flash cards every day. This is a realistic target for me including the time I read more materials as reference.
  2. I kept track of how many flash cards I have completed every day.
  3. I give myself some buffer just in case there is surprise in our life which takes up some time and delays my learning plan. We should accept sometimes we cannot complete our target as planned.

So I completed all 300 cards today! I have to say this is only the first round and I will repeat the whole process for the second or even third time.

I feel satisfied from every small achievement. Completing all the Flashcards is one of them.

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What roles do we need in a data science team?

When we think of building a data science team for your business, the first role we will want to have is data scientist. Is it good enough to have data scientists only? What else? And even only for data scientist role, what kind of skills do we need from them? Here we only talk about a data science team outside of the companies who create data science algorithms like Google, Facebook, or BAT.

Data scientist specialized in structured data — if your business mainly deals with structured data, they are the people you need. They will know supervised and unsupervised machine learning algorithms, including but not limited to naive bayes, linear regression model, logistic regression models, tree-based models, clustering algorithms, fully-connected neural networks, topic modeling methods. Within a good understanding of these models, they should be able to pick the right one.

Data scientist specialized in unstructured data— if your business mainly deals with unstructured data, like text or image, they are the people you need. Take image as example, their knowledge should cover convolutional neural networks, including the typical architectures like ResNet, EfficientNet, DenseNet, InceptionNet. There are other more basic architecture like VGG, which is less used nowadays. If they know these networks, and the commonly used activation functions, loss functions, optimizers within a neural network, they should have the knowledge to select a good one / ones.

Data engineers — they are the people who are programmers, who know how to deploy a data science model. It is a quite important but sometimes undervalued role. They should architect the CI/CD pipeline, find out the best way to expose the model result to the users. They don’t need to know the statistics behind the model, but they know the programming language, like Java, node.js, react.js to build the front-end and back-end. If this model is going to be deployed into the cloud, they should be familiar with the services provided by AWS, Azure, or Google cloud to host the models.

Lead who understands business needs — It is all about managing expectations of stakeholders! The gap between a business problem and viable technical solutions sometimes can be huge. How to translate the business problem into a data science problem, how to explain what can be done what cannot be done, how to get the stakeholder’s buy-in is equally important than creating the right data science model.

Sometimes a person may have multiple skills. It usually happens when this person is more senior. It is also rare to have all of them at the very beginning. So who to hire first is important. I will share some of my thoughts about who will be the first one to hire in my next post.

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What are the top 10 machine learning questions I ask during a Data Science interview?

Recent 2 years in my career, I have been part of strategy making and recruiting new team members. The most frequent role I interviewed is Data Scientist. I summarized the top 10 questions I always ask. In my next few posts, I will give my answer separately.

  1. What is logistic regression model? When do you use it?
  2. How do you interpret R^2 and p-value?
  3. Why random forest is called “random”?
  4. How is decision tree built? How do you select the next node?
  5. What is bootstrapping / bagging? What is out-of-bag error?
  6. What is the support vector in SVM?
  7. What is PCA? How do you select PCA? What is the limitation of PCA? What are the precautions before apply PCA?
  8. What are the commonly used regularization methods?
  9. What is Bayesian theorem? What is the assumption?
  10. What will you do to avoid overfitting?

These questions focus on the basics of statistics. I do believe it is important to be able to answer them. It definitely demonstrates you don’t only know how to call a library but also know why. But at the same time, I would say, more than half of the candidates who claim themselves applied machine learning models successfully are not able to give me good answers. Maybe you can share with me your thoughts.

As I promised, I will give my answers in the next few posts.

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What does a typical day look like for a data scientist?

Before you become a data scientist, maybe you have asked your friends or yourself this question: what is a typical day look like as a data scientist? My answer might not be representative enough or general enough, but at least I can give you some idea.

I work in a data science / consulting unit in finance sector. My typical day looks like this:

9:00– come to office / come to my home office during COVID. Get a coffee ready. Open up my Outlook and One Note to get the TODOs.

9:30 ~ 12:00–struggle with bugs in my code, switch between “git pull, git status, git commit, git push” and merge conflict error message.

12:00 ~ 13:00– Lunch. Grab food / restaurants near office. In our company, we have this culture of having lunch appointment with colleagues from other units. This is a good chance to exchange our ideas, share some work / life news, and socialise with other colleagues.

13:00 ~ 18:00– besides the normal coding stuff, there are more meetings (internal or with clients) because afternoon is the overlapping time period across different time zones.

18:00 ~ 19:00– reply some important and urgent emails before I call it a day for my office work.

19:00 ~ 21:00– dinner / take a break

21:00 ~ 23:00 — self-development activities. There are usually the activities I set for myself, like reading books, writing blogs, refreshing on some statistic basics, etc. I found it a good way to keep my knowledge refreshed all the time and be ready to answer any questions whenever I need to.