Data Scientist Roadmap

DS interviews often test whether you can make decisions with data under ambiguity.

What DS loops commonly include

• metrics and experiment design
• SQL / analysis
• product sense / case studies
• sometimes modeling
• storytelling and stakeholder influence

Core competencies

1) Metrics and experiments

• define success metrics and guardrails
• power/variance intuition
• A/B testing pitfalls

2) Causal reasoning intuition

• confounding, selection bias
• when correlation is enough vs not

3) Communication

• explain results clearly
• tie analysis to business decision

10-week plan

Weeks 1–2: SQL + analysis

• 3–4 timed SQL drills/week
• practice writing “one-page” conclusions

Weeks 3–6: Experimentation

• 1 case/week: metric definition + experiment plan + pitfalls

Weeks 7–8: Product sense

• practice turning vague goals into measurable plans

Weeks 9–10: Mock loop

• case study + SQL + behavioral

Failure modes

• metric confusion (optimizing a proxy that harms long-term goals)
• p-hacking / post-hoc stories
• unclear narrative and recommendations

Archetype adjustments

• FAANG/high-growth: large-scale experimentation cultures; speed and rigor.
• Enterprise: domain and stakeholder management may matter more.
• Automotive/industrial: safety/regulatory context can constrain experimentation; offline evaluation may dominate.