Tag: job prospects

How to Find a Job: Short Version in Only Three Points

Photo Credit: M ACCELERATOR

This is the short version of how to find a job. You can read my entire series here for a longer one.

Point 1: Set an amount of new people you can reach to a day

I recommend 4-5 people a day; 2-3 if you already have a full-time work.

->) This refers to starting a conversation, including people like recruiters who reach out to you, but not a follow-up with someone who you have been talking to for a while.
->) When someone recommends you talk to someone else, I usually count that, but it’s up to you (like later rounds of a job interview may not count; a judgement call).
->) Finding a job is a marathon, not a sprint. Snowball (aka increasingly exponentially): Talking to people leads to talking to more people, who recommend talk to more people. It may take a few weeks to build.

Point 2: Set an amount of jobs to apply in one day

I recommend applying to 10 jobs online a day.

->) For online job applications, quantity over quality works better, because of the way that the internet gamifies applications. Now, many applications won’t get back to you, so you have to apply a lot. It used to be the case that you might hear back from 10% of online applications, but the percentage may be even lower now.
->) Easy apply on LinkedIn is your friend.
->) Many job application platforms like LinkedIn notice that you are applying to jobs and are more likely to put on top of the list for recruiters looking for roles. Thus, sometimes quickly applying to many jobs on them in itself is a good thing even if you aren’t interested in those specific roles.
->) This is more important than applying online. Usually I find about two thirds to three quarters of my interviews are from networking connections rather than applying to jobs online.

Point 3: Reach out to people to talk to them

->) These are the best people to reach out to in order, those most likely to get back to you:
1) People you know and the people they know (friends, family, acquaintances, former classmates, etc.): Ask people you know if they know anyone in the fields you are interested in. Studies show that those most likely to coordinate a job are technically not friends or family but the acquaintance your friend and family members knows from “the whatever” two years. Ask for your people if they know people doing something similar to what you do.
2) Alum from any of your schools: It does not matter the year, even if they graduated decades before you. The perceived commonality of having attended the same schools means they are more likely to respond to you.
3) Internet searches on platforms like LinkedIn: For example, you can search for people in your area who work one of the jobs you would like, or anywhere. You can look up companies like those you are interest in and try to connect with people who work at that company (easier to lead to jobs with smaller companies).

->) What to say when reaching out to people:
Basic ask: Request to meet with them to learn about their experiences (the technical word for these are “informational interviews”, but I never actually use the phrase when talking to a regular person)
a) Prepare an introductory message template to use. Two important rules I follow:
Rule 1) Be succinct: Rarely more than 3-4 sentences. All your experiences, refine to a sentence or two.
Rule 2) Cut to the chase. State your request in the first sentence (after any greeting you have).

->) This is a basic template for an introductory message:

Hello,

I am passionate about _ [your desired field], and I would love to learn more about your experiences at _ [where they currently work] as _ [their title or describe their role]. Is it okay if we schedule a time to talk in the next two weeks to learn about your experiences?

More context about me: [Write 1-2 sentence bio that explains why you are interested in that field.]

Sincerely,
Stephen

Second example specifically for when you are transitioning from one field to another field:

Hello,

I have been working as a _ [your current field], but I have been trying to learn more about [the field they work in]. It seems like you do rather interesting work as a _ [their role] at _ [their company], and I would love to learn more about your experiences. Would you it be okay to schedule a time to talk in the next few weeks?

More context about me: [Write 1-2 sentence bio that explains why you are interested in transitioning into this field.]

Sincerely,
Stephen

You will refine these templates over time based on what seems to work for you and for that specific industry.

->) LinkedIn templates: You can include a message when connecting with people, but it has a very small character limitation, so I synthesize my “email template” above into something smaller that can fit their character limits. Then, for those who connect, I will write use the email template above as my basis for writing them a follow-up LinkedIn message.

->) People can choose whether to respond. It’s fine if they don’t have time. Don’t hound people. I don’t follow-up at all if people don’t respond to me. They may have better things to do with their life than talk to me, and that is fine. I focus my energy on those who apply and continue reaching out to more people if I need to talk to more people.

->) One important rule: Don’t formally ask people for a job. That puts people on the spot. They will know from such a message that you are looking for a role and will bring up a good role they have. There are a few subtle questions that draw the conversation towards jobs without asking explicitly. These work much better. Examples: “What does an entry position typically look in [the field they work in] or at [their company]?” or “How did you first enter this field? What kind of role did you have first, and how did you find it?” If they click with you, they may say their organization has a role, or that they may say they know someone who has an open position and offer to introduce you to them. They may also not say anything, often because they don’t know any roles. That’s fine.

->) Always ask: “Is there someone else who does this kind of work that you would recommend I talk to?” They may offer to introduce you to their friends. This keeps you meeting new people. Eventually one of whom will have a job for you.

With this, good luck. It’s a marathon not a sprint, so make you pace yourself, take stock, and prepare yourself for the adventure and inevitable frustration of rejection. If you feel discouraged, remember pretty much everyone has at least once when applying for a new job (for me, at least seventeen times).

Is data science still the sexiest job?

Photo Credit: Mahdis Mousavi

In 2012, this Harvard Business Review article argued that data science will be the sexiest job in the 21st century. At the time, data science was new and unheard of, with companies eager to use data scientists to revolutionize their practices. Is it still the sexiest job now? Well sort of, but not really. The field has gone through some significant transformations since these “wild west” early days. Now, data science as a discipline has become more streamlined and specialized.

Often key data scientists have slightly different titles like machine learning specialist, data engineers, etc. Machine learning and AI technology have changed the way data is processed and analyzed. This has automated parts of the tasks that data scientists have spent a long time working on, such as data cleaning (which still can take a long time) and initial data exploration, shifting the work necessary for humans to perform in the field to more specialized and fringe tasks. For example, many data scientists have become machine learning specialists focusing on fine-tuning these models or communication specialists focusing on how to use their business expertise to communicate complex findings with stakeholders and help decide what they should do about the results.

I think more than the technology, what has driven the specialization is the routinization of data science processes within an organization. Gone are the days of a lone data scientist at a company doing cutting edge work by themselves just figuring out what is possible. Data science as a field has fallen within the discipline and expectations of corporate bureaucracies. In its early stages, most data scientists worked alone or in small teams doing pioneering, experimental work figuring out how to apply the tools of the field to their organization in ways that people did not know were possible. That can still be the case. In every job I have had as a data scientist, for example, I have been the first data scientist in the entire organization or specific department I work in. But this is increasingly rare. Data science is now mostly one department at an organization, doing important but predictable routine work. All white collar professions get grafted in the “corporate machine” like this overtime.

Recent AI technology has contributed to this too by automating many of the low-level data processing and analyzing tasks so that non-specialists can perform them on their own. This is great, increasing the accessibility of tasks once considered obscure or even “magical” by regular people. Back in the day, to do much of any data modeling, you had to code it yourself, requiring a level of programming knowledge that was beyond a typical office worker or manager. That’s why they needed to hire a data scientist to analyze the data themselves. I hope in the long run using AI tools to tinker with data themselves and try out different theories will increase the data literacy and skillsets of regular professionals. It also means that data scientists are increasingly spending less time on these tasks and have moved to more complex, specialized work that still require quite a bit of technical human thinking.

Another factor that has driven this routinization is the increase in the number of people studying and doing data science. As demand for data science increase, more people have tried to become a data scientist, whether by receiving a degree in it or transitioning their careers into the field. This has led to more data scientists in the market. If this trend continues, eventually the field will become oversaturated, but the demand still seems to be higher than the supply, with more open jobs than people able to fill them.

This has still redefined what data science is. When many people join a field, it becomes difficult to maintain the same level of pioneering eclecticism. Instead, the types of tasks people do become routinized and standardized to provide consistency for a larger number of people, paralleling the transformation Max Weber describes religious movements undergoing from a charismatic leader to a routine social institution.

All of this leads to the current state of data science. This is not necessarily bad, but it is different. So, is data science still the sexiest job? Yes and no. Some of its specialist roles like machine learning specialist, I think, better maintain the excitement and cutting edge of that moniker. It’s still in high-demand, however, a fine field to work in.