Data Scientist II

Years of Experience: 3 to 4 years of non-internship professional data science or ML experience in fast-paced product startups or high-scale tech enterprises.

Experimentation & Causal Inference: Solid command of A/B test design, power analysis, and quasi-experimental methods (diff-in-diff, instrumental variables, synthetic control), including awareness of interference in marketplace/network settings.

ML & Optimisation Depth: Strong grounding in forecasting and at least one of operations research / reinforcement learning applied to allocation, matching, or pricing problems.

Feature Engineering: Proven ability to build, select, and maintain features from large, messy, real-world data.

Production Engineering: Comfortable deploying, monitoring, and maintaining ML pipelines, with the engineering discipline to keep models reliable in production.

Technical Toolkit: Fluent in Python and SQL, with the ability to work efficiently against large-scale data.

Problem-Solving Mindset: A knack for thinking from first principles and a track record of delivering high-quality work while balancing trade-offs like reliability, latency, and interpretability.

Iterative Mindset: A bias towards shipping early and iterating; a belief in small, incremental changes over large, multi-quarter undertakings.

Education: Bachelor's/Master's degree in Computer Science, Statistics, Engineering, or an equivalent quantitative field.

Data scientists with hands-on experience in quick commerce, marketplaces, logistics, ride-hailing, or on-demand delivery, who understand two-sided supply/demand dynamics.

Those with NLP / LLM experience — intent classification, entity extraction, embeddings, or conversational/voice data — directly relevant to Butler.

Engineers comfortable with streaming/big-data tooling (Spark, Kafka) and real-time inference.

High-agency individuals who treat their models as products and collaborate well across conflicting perspectives.