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Career Strategy Template

A structured framework for career planning during job transitions. Includes the Domain-First Prep process for interview preparation, T-Shape Audit for identifying knowledge gaps, and productivity systems for maintaining momentum.

How to use: 1. Fill in the "Current Direction" section to clarify your path 2. Use the Domain-First Prep process before each interview cycle 3. Run the T-Shape Audit to identify and close knowledge gaps 4. Adopt the MVD (Minimum Viable Discipline) system to maintain consistency


Current Career Direction

Decided Path

[Your target role type] -- Use first year to [primary goal] -- Reassess at 12 months

Critical Timeline

Deadline What Status
[Date] [Milestone] [Status]
[Date] [Milestone] [Status]
[Date] [Must-have outcome] [Status]

Compensation Targets

  • Minimum base: [Amount]/month ([Amount]/year)
  • Target total comp: [Amount]+ (base + bonus + equity)
  • Must have: [Critical benefits -- insurance, WFH, etc.]

Current Pipeline

Priority Company Why This Role
1 [Company A] [What moat/capital it builds]
2 [Company B] [What moat/capital it builds]
3 [Company C] [What moat/capital it builds]

Three Career Moats (Each Role Type Offers One)

Role Type Moat Type What You Become
Big Tech / High Brand Technical depth + brand "[Domain] builder at [Company] scale"
Consulting / Varied Technical breadth "Advisor who's seen N+ architectures"
Domain Specialist Deep expertise "[Domain] expert" (transferable within vertical)

All types are valid. No wrong choice -- each compounds differently over time.


Domain-First Prep Process

This is the core interview preparation framework. The key insight: prep should start from the team's business function, not from generic ML study guides.

The Process (Run for Every Interview Pipeline)

Step 1: Map the Org's Core Function

What does this team/org actually do? Not the JD buzzwords, but the business function.

Org Type Business Function Core Problems
Sales org Revenue generation Forecasting, demand planning, revenue analytics
Risk/Compliance Loss prevention Anomaly detection, regulatory reporting, explainability
Product org User engagement A/B testing, recommendation, engagement metrics
Ops org Efficiency Optimization, logistics, real-time systems
Infrastructure Platform reliability Scalability, monitoring, deployment
Research Innovation Novel methods, benchmarks, publications

Step 2: Derive the Likely Technical Domains

What ML/DS techniques serve that function?

Business Function Key Technical Domains
Revenue/Sales Time series forecasting, causal inference, MAPE, Prophet, hierarchical models
Risk/Compliance Rare-event classification, explainability, recall-focused metrics, graph ML
Product Experimentation platforms, bandits, ranking models, recommendation
Ops Optimization, routing, real-time inference, constraint satisfaction

Step 3: Audit Your T-Shape Against Those Domains

Can you name the standard tools, metrics, and approaches for each relevant domain? (See T-Shape Audit below.)

Step 4: Close Fundamentals Gaps Before the Interview

Even 2-3 hours of focused study converts "can't name it" into "haven't built it but understand the landscape." That's the difference between a concerning gap and an acceptable one.

The meta-lesson: Domain analysis should happen when you start prepping for the company, not the night before. If the JD says "Sales org" -- forecasting prep should start immediately.


T-Shape Audit

The T-Shape Concept

Senior-level interviewers expect T-shaped knowledge: deep in your specialization, broad enough to speak intelligently about adjacent areas.

What's Acceptable What's Concerning
"I haven't built forecasting systems, but here's how I'd approach it..." "I don't have experience with that" + can't name tools/metrics
Proactively acknowledges gap + asks good clarifying questions Gap in both experience AND fundamentals
Limited hands-on but understands the landscape and tradeoffs Can't demonstrate short ramp-up distance

The key distinction: There's a difference between "I haven't built X" (experience gap -- fine) and "I can't name the standard tools/metrics for X" (fundamentals gap -- concerning). The first shows specialization. The second signals narrow learning habits.

Breadth Audit Template

Customize this for your field. Three separate columns track the real state -- having notes does not equal having learned it:

  • Coverage: Do you have reference material on this topic?
  • Readiness: Can you answer conceptual + technical questions under interview pressure?
  • Experience: Hands-on project work (strengthens answers but not required for the T-shape bar)
Domain Fundamentals Bar Coverage Readiness Experience Notes
[Your core specialty] [Key tools/metrics]
[Adjacent domain 1] [Key tools/metrics]
[Adjacent domain 2] [Key tools/metrics]
[Adjacent domain 3] [Key tools/metrics]
[Adjacent domain 4] [Key tools/metrics]

Example for ML/DS roles:

Domain Fundamentals Bar Coverage Readiness Experience
Classification & rare events Precision/recall, class imbalance, SMOTE
NLP / LLMs / GenAI Transformers, RAG, evaluation, agents
Time series forecasting ARIMA, Prophet, MAPE, hierarchical models
Recommendation systems Collaborative filtering, embeddings, ranking
Causal inference A/B testing, DiD, uplift modeling
MLOps / production CI/CD, drift, monitoring, deployment
Experimentation Statistical significance, power analysis, sequential testing

Readiness Pipeline

The honest path from gap to battle-tested:

No material --> Content added --> Read through --> Q&A drill --> Interview-ready --> Project-proven
Level What It Means Can You...
No material No reference material exists --
Content added Material captured, not yet studied Look it up if asked
Read through Studied the material once Recognize terms, explain at high level
Q&A drill Practiced with mock questions Answer conceptual + technical questions under pressure
Interview-ready Can discuss fluently without notes Name tools, articulate tradeoffs, connect to experience
Project-proven Built something end-to-end Share war stories, debug edge cases, explain implementation decisions

Each step is distinct. Don't skip steps or mark something ready just because notes exist. Project-proven is the gold standard -- it separates "I've read about X" from "I've used X in production and here's what surprised me."

Using the Audit

Before a new interview pipeline: Run Domain-First Prep (above) to identify which domains matter, then check readiness for those domains.

Between interviews: Pick one gap per week to move through the readiness pipeline.

Goal: All domains at minimum -- material exists + reviewed + can articulate standard tools and tradeoffs under pressure.


Interview Lessons Framework

LLM Self-Assessment Calibration

If you use LLMs to analyze interview transcripts or mock interviews, be aware of systematic biases:

LLM Bias What Happens Correction
Social signal over-weighting Positive comments and engagement read as pass signals These are politeness norms, not evaluation signals
Domain-agnostic scoring Scores breadth equally regardless of role focus Weight gaps in the role's core domain 2-3x higher
Compensatory assumption Assumes strength in area A compensates for gap in area B Interviewers evaluate each competency against a bar, not on average
Candidate perspective blindness Dismisses candidate's gut feeling as "underestimation" You were in the room -- your read on interviewer reactions has signal

Rule of thumb: When LLM estimate diverges significantly from your self-assessment, your self-assessment is probably more calibrated -- you were in the room, the LLM only read a transcript.


Productivity Systems

Minimum Viable Discipline (MVD)

The core insight: consistency beats intensity. A system that survives bad days is better than one that only works on good days.

Bad Day Baseline (10-20 min) -- "Keep the chain alive"

  • Add 3 lines to documentation
  • Send one outreach message
  • Practice one interview story out loud

Normal Day (60-120 min)

  • 10 min: choose 1 outcome
  • 45-70 min: build/write
  • 10 min: document/commit

High-Energy Day (deep work)

  • 2 x 45-50 min focused blocks
  • No "catch up" -- high-energy is bonus, not debt repayment

Traffic Light Tracking (Not Streaks)

Streaks create shame spirals when broken. Use traffic light tracking instead:

  • Green: Full session (60-120 min)
  • Amber: Bad-day baseline (10-20 min) -- counts as a win
  • Red: Nothing

Goal: Reduce red runs, not maximize greens.

Relapse Plan (After Missing 2-3 Days)

  1. Say: "I'm restarting, not catching up."
  2. Do the smallest task (10 minutes)
  3. Log a win ("Returned")
  4. Pre-load tomorrow: open the repo + leave a note for next step

Success metric: Restart speed, not streak length.

The Core Loop to Break

Rigid/unrealistic targets --> miss --> shame --> avoidance --> inconsistency

This pattern resurfaces whenever: - "Learn everything" becomes the new rigid target - Rejections trigger shame spirals - New job pressure exceeds capacity

Recognition is the first step to breaking the loop.


High-Leverage Habits

  1. Sleep protection -- Fixed caffeine cutoff, phone out of reach, wind-down routine
  2. Anti-shame tracking -- Traffic light system (amber counts as a win)
  3. Environment design -- Keep a "NEXT" file with the next 3 micro-steps. Reduce friction to start.
  4. Health-first days -- On bad health days, do bad-day baseline only. No planning, no guilt.
  5. Weekly closure ritual (20 min) -- Choose next week's 3 outcomes, delete the rest

Useful Scripts

30-Second Pitch

"I'm a [role] who's strongest at [core strength -- what makes you different]. Over the past [timeframe] I've [1-2 key accomplishments]. I'm looking for [what you want next] at a company where [what matters to you]."

"Why Are You Leaving?" Answer

"After [X years] at [company], I've grown from [starting point] to [current level/scope]. I'm looking for [what's next -- depth, breadth, domain, scale] at a company where [specific draw]."

Boundary Script (Protecting Productive Time)

"Given current constraints, I'm focusing on one deliverable at a time. If priorities change, I can swap -- but I can't stack."


Decision Frameworks

The "Impact vs. Cutting-Edge" Tension

Many people feel pulled between (a) state-of-the-art technology and (b) impact-driven work. This is often a false dichotomy.

What actually sustains motivation (look at your own evidence):

Experience Sustained? Why?
[Past role 1] Yes/No [What about the daily work energized or drained you]
[Past role 2] Yes/No [What about the daily work energized or drained you]
[Current situation] Yes/No [What about the daily work energizes or drains you]

Bottom line: Motivation that depends on a narrative ("I'm saving the world" / "I'm doing cutting-edge AI") fades. Motivation from the actual daily work -- shipping, iterating, seeing results -- sustains. Pick the role where the daily work energizes you, and let the narrative follow.

Regret Asymmetry Analysis

When choosing between options, ask: which choice is easier to recover from if it turns out wrong?

Scenario Regret Level Recovery Path
Take Option A, miss Option B's niche? [High/Low] [Can you get back to B later?]
Take Option B, miss Option A's advantages? [High/Low] [Can you get back to A later?]

One option's value is usually more guaranteed (e.g., brand + comp are structural). The other's value is more conditional (e.g., depends on team, manager, scope). Factor this asymmetry into your decision.


Key Quotes

"Outperforming in a weak org buys you internal power, not long-term freedom."

"It's not 10 years of any work. It's 10 years of compounding the right capital."

"Career capital doesn't compound from being the best at one thing. It compounds from sitting at a rare intersection."

"Most people overestimate what they can do in one year and underestimate what they can do in ten years."


Review this template at the start of each job search cycle. Update with lessons learned after each interview round.