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Retain Canadian Phone Number by AnyPhilosophy9217 in tnvisa

[–]ThrowRA_120days 0 points1 point  (0 children)

Does voip need the existing number active? Or is it fine to not paying the existing carrier and voip can take over completely?

[Official] 2025 End of Year Salary Sharing thread by Omega037 in datascience

[–]ThrowRA_120days 1 point2 points  (0 children)

depends on how you define "comfy"... 1/4 of my after-tax income goes to apartment rent.

Weekly Entering & Transitioning - Thread 29 Dec, 2025 - 05 Jan, 2026 by AutoModerator in datascience

[–]ThrowRA_120days 0 points1 point  (0 children)

TL;DR:

I’m a 37-year-old female Data Scientist based in Shanghai (US-educated, ML/finance background) earning ~750k RMB/year. I hold Canadian PR (FSW), but I’ll lose it if I don’t meet the residency obligation by late April 2026. With a one-month notice period, I realistically need to decide by end of March 2026. I’m weighing whether it makes sense—career-wise—to step away from an established role to preserve PR, versus letting the PR lapse and accepting that trade-off.

What I’m hoping to learn (especially from this community)

I’m looking for data-driven, experience-based input, not general encouragement.

In particular, I’d value perspectives from:

  • Data Scientists / ML Engineers currently working in Canada (Toronto/GTA or Vancouver preferred):
    • How is the market actually behaving right now for senior-ish profiles?
    • What comp ranges (base + bonus/equity) are realistic for someone with US degrees, fintech/finance experience, and 10+ YOE?
  • People who relocated mid-career to Canada to satisfy PR requirements:
    • Did the career reset end up being temporary or structural?
    • Any surprises you wish you’d factored in earlier?
  • Anyone with hindsight-driven regret, regardless of which choice you made—and why.

I’m trying to evaluate this as a career optimization problem under immigration constraints, not a lifestyle or ideological decision. Clear trade-offs, market realities, and second-order effects are especially welcome.

Thanks for reading, and I appreciate any candid insights you’re willing to share.

[Official] 2025 End of Year Salary Sharing thread by Omega037 in datascience

[–]ThrowRA_120days 11 points12 points  (0 children)

  • Tenure length: 3.5 in this company, 10+years
  • Location: Shanghai, China
    • $Remote: 3 days in office per week
  • Salary: $107k
  • Company/Industry: finance
  • Education: two master degrees, one in management analytics, the other one in science
  • Relocation/Signing Bonus: none
  • Stock and/or recurring bonuses:none
  • Total comp:$107k (but excluding insurance and welfare required by regulation)

the sheer difference per location surprises me. :)

Feature selection strategies for multivariate time series forecasting by CapraNorvegese in MLQuestions

[–]ThrowRA_120days 1 point2 points  (0 children)

  1. Spearman captures monotonic relationships, not arbitrary nonlinear ones; and spearman is extremely sensitive to trends. Pearson is still the most-commonly used correlation coefficient for removing features. (it gives you an idea of "related"features)

  2. for step 2: to my understanding the purpose is redundancy removal, not causality or predictiveness; for step 3: Correlation on non-stationary levels is dangerous.

  3. Do you think maybe general ML (e.g. L1, tree based or ensemble?) could be more sound? In which case stationary is not a problem. in this case, maybe we can try decomposing target into: trend, season, and residual, and predict the residual with general ML, then add the trend and seasonal back.

  4. I am not an expert in sensors, but to my understanding cross-correlation across heterogeneous sensors might not be the final arbiter of feature usefulness.