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How to win the Strategy & Ops interview process in Big Tech
Landing strategy & operations jobs in big tech is challenging.
But the right process map and mental framing will give you a significant edge.
Four to six rounds. Take-home cases. Live pressure-test interviews.
For me, the last intense interview process had been several years ago.
So at first, I felt daunted and pressured cause I really wanted the role.
But somewhere along the way, I managed to shift my mindset:
Instead of treating the interviews like hurdles, I started treating them like windows.
Windows into what the job would actually feel like—how I’d think, who I’d work with, and whether I’d enjoy each part of the work.
That mental shift took away most of the pressure.
The second key unlock for me was to build a clear understanding of the process, which I am sharing below.
I hope sharing this will increase your chances of landing your dream offer. And once you get there, check out my previous post about how to optimize the offer process here.
Strategy & Operations Interview Process Map:
The process usually consists of 4-6 interview rounds, each testing different things:
1. Hiring Manager Intro & Strategic Deep Dive
Goal: Assess role fit, scope alignment, and your approach to real-world strategic problems.
Typical format: Conversational but pointed — often includes light case-style questions.
What they test:
Strategic thinking in ambiguous contexts
Ability to structure high-level decisions
Initial cultural and role alignment
My Tip: Ask the recruiter beforehand if this round includes case questions — they’ll often tell you. Knowing the format (behavioral vs. case vs. mixed) allows for sharper prep.
2. Take-Home Case Assignment
Goal: Assess your ability to break down ambiguity, analyze large datasets, and make clear, defensible recommendations.
Typical format:
24–48 hours to submit slides or a short memo
Prompt could cover market entry, growth diagnostics, retention strategy, etc.
What they test:
Structured thinking and prioritization
Analytical rigor (you may get a messy 10k–20k row dataset)
Clarity in communication
My Experience: Take-home cases can be intense (e.g., weekend work) with limited direction and a large datasets. The key was to stay structured, identify the highest-leverage insight, and show you are willing to update your priors based on new data.
3. Live Case Q&A (Based on Take-Home)
Goal: Pressure-test your thinking, frameworks, and adaptability.
Typical format:
30–60 min discussion
One or more interviewers will challenge your assumptions, ask “what ifs,” and simulate pushback.
What they test:
Depth of understanding
Comfort adapting your approach
Ability to handle stakeholder-style back-and-forth
My Note: This round is less about being “right” and more about being thoughtful, flexible, and open to feedback. Be prepared to pivot your answer when new constraints are introduced — just like in the real job.
4. 1-2 Behavioral Interviews: Strengths & Operating Style
Goal: Understand how you operate independently, handle ambiguity, and drive impact. Test stakeholder management, influence, and alignment skills.
Typical format: STAR-based questions focused on ownership, bias for action, and initiative.
What they test:
Ability to lead without authority
Navigating conflicting incentives and priorities
Driving execution across teams
Alignment with team’s working style
My Takeaway: Overall more relaxed part of the process but focus on clear and concise communication. I leaned into examples where I defined process, built trust with cross-functional leads, and navigated complexity to ship results.
5. Role-Specific Scenario Interview
Goal: Assess how you'd operate in a real-world business situation tied to the role.
Typical format:
Open-ended situational prompt (e.g., “DAU is dropping — what do you do?”)
No perfect framework — just structured reasoning, good judgment, and execution thinking
What they test:
Practical prioritization
Strategic vs. operational trade-offs
Ability to take action with limited context
My Observation: These rounds are less academic, more applied. Show you can go from problem to plan — even without full data. Structure matters, but so does good judgment.
6. (Optional) Final Round with Leadership or Recruiter
Goal: Final fit check — confirm mutual alignment on working style, motivation, and team culture.
Typical format: Informal conversation
What they test:
Culture fit and enthusiasm
Clarity on your motivations
Readiness to join the team
Tip: Purely negative screening (i.e., don't give them any red flags). Come with thoughtful questions about the team’s priorities, culture, and strategy. It shows you’re already thinking like a team member.
Market Pulse: Key News You Need to Know
1. Columbia’s STAR AI Spurs First‐of‐Its‐Kind Fertility Success
What Happened: Columbia University researchers adapted astrophysics algorithms to create STAR, an AI system that scanned eight million microscopic images in under an hour to identify 44 viable sperm cells—enabling a couple to conceive after an 18-year struggle .
Why It Matters: By reducing IVF‐related time and labor from days to under an hour and slashing per-cycle costs from $15K–$30K to around $3K, STAR could democratize access to fertility treatments and address plunging global birth rates .
Who It Affects: Infertility specialists, clinics, prospective parents, and insurance providers exploring scalable precision‐medicine solutions.
What’s Next: Expect accelerated clinical trials, partnerships with fertility clinics, and further adaptation of STAR’s imaging pipeline to other high-throughput diagnostics.
2. Microsoft’s MAIDXO Doubles Down on AI‐Orchestrated Diagnostics
What Happened: Microsoft launched the MAI Diagnostic Orchestrator (MAIDXO), a multi-agent system that simulates a virtual medical team—hypothesis generation, test selection, cost monitoring—and, when paired with OpenAI’s latest model, achieved an 85.5% correct diagnosis rate versus 20% for human experts on complex cases .
Why It Matters: By cutting per-case costs to roughly $2,400—largely through reduced unnecessary tests—MAIDXO promises both clinical accuracy and significant healthcare savings, tackling overtreatment and underdiagnosis simultaneously .
Who It Affects: Hospitals, diagnostic labs, healthcare payers, and policy-makers aiming to optimize care pathways.
What’s Next: Broader pilot programs, regulatory engagement for AI-driven diagnostics, and expansion into specialized fields such as oncology and rare diseases.
3. Chai Discovery’s Chai2: “Photoshop for Proteins”
What Happened: OpenAI-backed Chai Discovery unveiled Chai2, an AI platform that designs novel functional antibodies from scratch. With a hit rate near 20%—a 100× improvement over sub-0.1% traditional methods—Chai2 delivers viable candidates in two weeks versus months or years .
Why It Matters: Drastically lower R&D costs and timelines could unlock precision medicines for rare diseases previously deemed unviable, remapping the pharmaceutical landscape toward economically feasible, bespoke antibody therapies .
Who It Affects: Biotech companies, CROs, academic drug‐discovery labs, and patients awaiting novel therapies.
What’s Next: Partnerships with major pharma, scaling to other biologics (e.g., enzymes, cytokines), and in vivo validation studies.
4. The “Triad” Compute Stack: ASICs, CXL, and Photonics Replatform AI
What Happened: As GPUs hit energy and utilization ceilings, AI leaders are pivoting to an integrated stack—custom ASICs for workload-specific acceleration, memory disaggregation via CXL for up to 30% TCO reduction, and optical I/O (photonics) to break copper interconnect limits .
Why It Matters: This one-way replatforming creates lock-in at the physical layer: partial adoption yields underutilized hardware and stranded investments, effectively mandating full Triad integration by 2031 to avoid obsolescence .
Who It Affects: Hyperscalers, cloud providers, chip vendors, and enterprises planning multi-year AI infrastructure roadmaps.
What’s Next: Surge in CXL controller fabs, national strategies for photonics manufacturing, and emerging ASIC startups aligned with major AI labs.
5. Oracle’s 2 GW Bet: Locking in the AI Compute Race
What Happened: From Nov 2023 to Jan 2025, Oracle committed over 2 GW of long-term data-center capacity—approximately $3 billion annually and surpassing its FY 2022 cloud revenue—anchored by an estimated $15–$20 billion, 15-year Crusoe contract likely tied to OpenAI projects .
Why It Matters: This audacious, decade-long leasing strategy signals a shift from agile, short-term capacity to massive, lock-in commitments—underscoring compute availability as a strategic asset in AI competitiveness .
Who It Affects: Data-center operators, AI startups seeking guaranteed power, investors evaluating cloud-infrastructure financing.
What’s Next: More hyperscalers embracing hybrid partnerships with unconventional miners and renewable providers, alongside advanced networking (e.g., ROSEP2) and ODM collaborations to control OpEx.
6. Democratizing AI Development: From Claude Artifacts to Gemini CLI
What Happened: Anthropic’s Claude Artifacts now lets users build custom AI tools (grammar checkers, summarizers) directly in Claude; Cursor’s AI agents enable remote coding tasks via web/mobile; Google launched Gemini CLI for free code analysis, app generation, and workflow automation—all lowering the barrier to AI-powered development .
Why It Matters: By embedding AI at every phase of software creation, these tools accelerate innovation cycles and empower non-specialists, presaging an “iPhone moment” for AI that makes advanced capabilities accessible to broader audiences .
Who It Affects: Developers, product managers, startups, and enterprises integrating AI into internal workflows.
What’s Next: Convergence of low-code AI orchestrators, native IDE integrations, and agent networks combining LLMs with real-time internet access.
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