Young Hands Club

Priority 1: Tasks 1, 2, 4, 5, 6, 7, 9
Priority 2: Tasks 3, 10, 12
Priority 3: 11

1. [ ] Answer to “What do I need first to be able to work on TMSR full time.”
   1. [2019-09-08 Sun] The plan is to summarise the discussions so far on this topic and expand on points that have not been touched (by me), and work towards a complete answer, considering the question explicitly. An existing draft is being updated and refined. I think I should share the updates as they are formulated rather than post a ‘final answer’ on the deadline of [2019-09-15 Sun]. Perhaps a good idea would be to post the draft tonight and review it in chan.
2. [X] Gather a list of companies in Calgary
   1. Used Google jobs to filter out data analyst roles in Calgary. There are probably some more, but the list is long enough to start with.
   2. Identified Recruiters/Head hunters from the list.
   3. Posted at http://s.ragavan.co/job-search/company-list.html
      1. The document is posted in the above format to make it easier to identify recruiters and priorities as well as TODO’s. The alternative was a page in the WP blog post. This would mean individually adding TODO’s, priority level, recruiter identification as text under each heading.
      2. The above approach also makes it easier to ask friends/contacts if they have contacts in these companies, and this has been initiated today, and has to spread to others.
3. [ ] Gather the job descriptions (jd) of each opportunity.
   1. [ ] Combine multiple job postings into a single generic checklist for each company.
   2. [ ] Differentiate data scientist and analyst skills under each company if applicable.
   3. [ ] extract a list of skill deficits from above checklist.
      1. [ ] Formulate plan of attack for deficit and post for review.
      2. So far – there are indications that only python is asked on the jobs in Calgary with no mention of R. In Toronto, there was a healthy mix, and often Python/R skills were asked. This may be a problem, since I am a lot more comfortable with R today, even to the extent of simple ML. While the algorithms are the same – there are signifcant differences in syntax between R and Python. The clear solution in terms of learning this deficit is the course(s) mentioned earlier (pending review of Python basics + others available for fundamental data analysis) – which will certainly suffice based on initial exploration (and compared to experience with other courses in the past).
      3. Another possible approach may be expanding the region of search beyond Calgary to the next nearest areas. These appear include R in greater number (from a very quick search).
4. [ ] Skim through jd’s, shortlist the profiles that are most aligned with current skillset.
   1. This has been initiated. Draft exploration will be posted tomorrow (rather than waiting to finish it all and then posting.)
5. [ ] modify resume aligned to the shortlisted jd’s.
6. [ ] Send out applications in (3)
   1. fwiw: some applications were sent out, with no modification of existing resume.
7. [ ] Gather contacts related to sent applications.
   1. [ ] Send out connection invite + ‘applied to this’ shout-out + try to schedule chat.
   2. Initiated contact with a guy (dads friend) at Enbridge – he is travelling and will respond on 23rd Sep. Being based in Calgary, and having worked at Ernst & Young till recently – he might have good advice.
8. [ ] Extra ‘interesting’ companies from the list (to begin with)
   1. [ ] Shortlist interesting companies
      1. Basis: general alignment with background, example Oil & Gas (eg customers of current company). Company profile and job descriptions that sound ‘better’.
   2. [ ] Gather information on the shortlisted companies, summarise and add to (2.3)
   3. [ ] Gather Contacts info via Linked in.
9. [ ] Formulate a simple, generic cover letter for online job portals
10. [ ] Recruiters
    1. [ ] Gather earlier contacts from head hunting companies from earlier efforts.
       1. [2019-09-08 Sun] This has been initiated. I have sent out emails / messages to earlier contacts in Ontario, whether they themeselves can help or re-direct me to someone in their Calgary office.
    2. [ ] Reach out them and identify correct local contact person in Calgary.
    3. [ ] Identify contacts in new recruitment agencies.
11. [ ] Review github website (https://shrysr.github.io)
    1. [ ] Re-align to emphasise data + analytics, supported by domain knowledge rather than the other way round as it is currently.
12. [X] Explore Alternative approaches:
    1. [X] FDM. Their M.O is to to take you in on a 2 year contract, and train you over several (6-10 iirc) weeks, while paying a stipend. They have a ‘data science’ cohort as well as a ‘dev’ cohort, with slight differences. Subsequently, you are posted with their clients after appropriate interviews with the clients. The salary is fixed over this 2 year period. The salary is obviously low for Toronto, however – this is as ‘guaranteed’ as it gets in terms of an entry and shift. I was on the verge of taking this up, when I got my current salt-mine offer. They do not have any office in Calgary.
       1. A possible approach may to fix up a spot with them and join them at the starting date of their next program. There are logistic irritations of shifting to Toronto involved, including finding a place to stay, etc. These are not insurmountable, though it would be better in general to not do this in winter.
       2. It is likely there is a stiff penalty for breaking the contract, though they did not share this with me despite repeated questions at the time. I should make a quick check if there is any correspondence/document I have missed in the summary above.
       3. In a away – this could be an answer to keeping my head down – getting minimal subsistence for 2 years and working on TMSR – and building skills for a big jump towards the end.
       4. One additional advantage is that once established via FDM – after the initial 2 year contract – it is possible to secure more jobs via FDM at much ‘higher rates of pay’ probably on a contract basis. The details of the last point is fuzzy in my memory – but this information can be obtained if deemed worthy of time.
       5. Overall – it seems this particular approach with FDM is best left as a last resort.
    2. [X] Explore existence of companies or bootcamps similar to FDM based in Calgary. In terms of overall job prospects (no. of jobs and companies and networking opportunities), as well as ‘settling’ – Toronto (12.1) is a better option for data science. A quick search indicates that there are no bootcamps in Calgary. There could be more options in Toronto – but overall, the bootcamp approach is flaky in terms of results, and (12.1) does not involve investment, other than shifting to Toronto.
       1. [X] Method Data Science: This is basically a remote internship where a fee has to be paid upfront, and then you are paired with a ‘mentor’ for industrial projects, covering about 1-2 months. This is an approach to gain project experience while being employed. This is not aligned to the current urgency in terms of results.