Introduce yourself and justify quant research fit

How to structure your answer (3–5 minutes total) - Part 1: 45–60s Intro - Now: Role/skills relevant to quant trading research (low-latency, distributed systems, data pipeline quality, simulation/backtesting). - Proof: One quantified achievement (throughput/latency/accuracy improvement). - Edge: Comfort with probabilistic reasoning and mental math. - Part 2: 60–90s Why this role, why Optiver - Role: Building reliable, ultra‑fast systems that turn research into P&L; tight feedback loops. - Optiver: Market making focus, research–engineering pairing, bias for truth/experimentation, speed with risk control. - Fit: You enjoy making decisions with incomplete data, optimizing latency vs. correctness, and measuring EV. - Part 3: 90–150s High‑stakes decision story (STAR/R framework) - Situation: Time‑boxed, incomplete info, material risk or P&L impact. - Task: Your clear objective and constraints. - Action: Options → selective data → mental math → decision. - Result: Outcome with numbers; reflection on what to improve. Mental‑math/estimation toolkit (use 1–3 in your story) - Order‑of‑magnitude checks: sanity‑check inputs/outputs. - Expected value (EV): EV = p(loss) × loss − p(gain) × gain; compare options. - Proportions quickly: x% of N → (x/100) × N; 0.2% of 1,000 ≈ 2. - Binomial standard error: SE ≈ √(p(1−p)/n); 95%CI ≈ p ± 2×SE. - Linear approximations: small deltas scale approximately linearly (e.g., 1 ms latency → ~k% hit‑rate change). - Square/square‑root anchors: √n for variance scaling; √(p(1−p)/n) for uncertainty. - Log/percent tricks: (1±x) ≈ e±x for small x; 7% rule of 70 for rough halves/doubles. Mini numeric example you can adapt - Suppose a venue’s market‑data feed shows gaps 5 minutes before open. You can either stay or failover to a backup with +2 ms latency. - Observed: 50 gaps in 25,000 messages → p̂ = 0.2%. - SE ≈ √(0.002×0.998/25,000) ≈ √(7.98e−8) ≈ 0.000283 → 95% CI ≈ 0.2% ± 0.057%. - Affected trades per minute on that venue ≈ 1,000; expected affected ≈ 0.2% × 1,000 ≈ 2 trades/min. - Cost per stale fill ≈ $50 ⇒ expected loss ≈ $100/min (CI roughly $71–$128). - Failover adds 2 ms; if 1 ms ≈ 1.5% hit‑rate drop, then ≈ 3% drop. If venue P&L ≈ $3,000/min at open → cost ≈ $90/min. - Compare: $100/min (stale) vs $90/min (failover); include tail‑risk of bursts → choose failover. Example answer (customize to your background) - Intro (45–60s): “I’m a software engineer focused on low‑latency, data‑intensive systems. Recently I led a C++/Python stack for an order‑book simulator and execution gateway, reducing p99 latency from 12 ms to 4 ms and improving backtest‑to‑prod hit‑rate alignment by 7%. I like turning probabilistic research into reliable, measurable systems and I’m comfortable doing quick EV and back‑of‑the‑envelope checks under pressure.” - Why this role, why Optiver (60–90s): “This role sits at the intersection of research and high‑performance engineering, where inches of latency and basis points of error materially affect outcomes. I enjoy building systems where we quantify trade‑offs—speed vs. accuracy, recall vs. precision—and iterate via tight feedback loops. Optiver’s focus on market making, direct collaboration between researchers, traders, and engineers, and the emphasis on truth‑seeking and decisive execution fit how I work. I want to own problems end‑to‑end, measure EV, and ship robust solutions that move P&L while managing risk.” - High‑stakes decision (90–150s): “Ten minutes before the open, we saw sequence gaps on a major venue’s market‑data feed after a config change. Options: stay on the primary feed and risk stale quotes, or fail over to backup with a +2 ms latency penalty. Time was tight: we needed a decision in under a minute. I pulled two fast numbers: gap rate and per‑ms cost. Logs showed 50 gaps in ~25k messages → p̂ ≈ 0.2%. With 95% CI roughly 0.14–0.26%, expected affected trades per minute ≈ 0.2% × 1,000 ≈ 2. If a stale fill’s adverse‑selection cost averages ~$50, EV loss staying put ≈ $100/min. From our past open stats, +1 ms costs ~1.5% hit‑rate; +2 ms ≈ 3% of venue P&L. With ~$3k/min on that venue during the open, failover cost ≈ $90/min. Given similar EVs but higher tail‑risk from bursty gaps, we failed over. We announced the decision, added extra monitoring, and scheduled a post‑open root‑cause fix. Outcome: stable open; P&L impact tracked near the $90/min estimate for ~6 minutes. Post‑mortem confirmed the primary feed config issue. What I’d do differently: pre‑define a runbook with EV thresholds, automate a 30‑second ‘gap‑rate CI’ and ‘latency‑to‑P&L’ calculator, and place a circuit‑breaker that triggers auto‑failover when gaps exceed a set CI bound.” Why this works - Shows clear objective, options, quick but defensible math, explicit assumptions, and tail‑risk reasoning. - Balances speed (≤1 min decision) with accuracy (CI and historical anchors). - Quantifies the outcome and provides a concrete improvement plan. Pitfalls to avoid - Vague stories without stakes or numbers. - Hand‑wavy math; always state assumptions and do a quick sanity check. - Collecting too much data before deciding; time‑box your evidence gathering. Quick checklist before you answer live - Intro: role‑relevant skills + 1 quantified win. - Motivation: role mechanics + firm fit + your operating style. - Story: situation → options → quick math → decision → result → improvement. - Use 1–3 mental‑math tools and say them out loud (EV, CI, proportional reasoning).