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Requirements:
You've led a data team inside a startup. You're a player-coach, comfortable
writing SQL one day, pushing back on C-level the next. Startup experience is essential.
You operate via contracts with source teams, not central ownership of every dataset.
You've held source teams accountable for data quality.
You're technically broad enough to make warehouse, ELT, and orchestration decisions.
Fluent in the modern data stack: a major BI tool (Sigma, Looker, Mode, Hex), dbt,
Postgres or Snowflake or BigQuery or Redshift, ingestion tooling like Fivetran.
You have a real point of view on AI in data work and can describe what you'd try first.
You connect data investments to revenue, cost, retention, and competitive position. You
bring questions, not just answers.
Nice to have
Insurance, Insurtech, or marketplace experience. Direct experience standing up a modern BI
platform.

Requirements:
Core Responsibilities
Design and implement end-to-end data digestion pipelines for complex unstructured and semi-structured inputs.
Integrate classical algorithms with LLM-centric workflows to produce high-quality contextual inputs for reasoning tasks.
Build systems for segmentation, structure extraction, semantic decomposition, embedding-based representations, and graph construction.
Develop agentic workflows that combine tools, retrieval, and reasoning for interactive customer experiences.
Design evaluation suites, datasets, metrics, and regression tests to measure quality, robustness, and performance over time.
Continuously refine workflows based on customer usage, failure analysis, and measurable improvements.
What We're Looking For
Required Qualifications
MSc (or equivalent experience) in Computer Science, Mathematics, Electrical Engineering, or a related quantitative field.
5+ years of experience in data science / algorithm development / applied research engineering roles.
Strong experience building complex algorithmic workflows beyond model calls - including multi-stage pipelines, feature extraction, structure inference, and optimization.
Deep understanding of LLM digestion concepts: RAG, context engineering, chunking strategies, retrieval and ranking, tool usage patterns, and reliability techniques.
Experience designing and maintaining evaluation frameworks for both classical algorithms and LLM workflows (offline + online, regression, benchmarking).
Strong programming skills in Python; familiarity with TypeScript is a plus.
Ability to thrive in a high-ownership startup environment: ambiguity, speed, responsibility, and continuous learning.
Nice To Have
Familiarity with vector databases and scalable retrieval architectures.
Familiarity with agent orchestration frameworks (e.g., LangChain) and tool-driven reasoning systems.
Experience deploying AI workflows into production with monitoring, guardrails, and iteration loops.

Requirements:
What Were Looking For
2-5 years of experience in one or more of the following:
Product / Business / Financial Analyst
Technical Product Support
Solutions Consultant
Product / FinTech Operations
Implementation Consultant
Experience working with SaaS or enterprise software.
Strong analytical skills and confidence working with numbers, financial data, and business metrics.
Excellent English (written and spoken) - mandatory.
Ability to understand complex business workflows and translate them into scalable product solutions.
Experience identifying product issues and recommending improvements.
Comfortable performing product validation and investigating functional issues.
Excellent communication skills and ability to collaborate across Product, Engineering, AI, and Customer Success teams.
High attention to detail and strong ownership.
Nice to Have
Experience in FinTech, Payments, Banking, Financial Services, ERP, or Enterprise SaaS.
Experience with AI products or LLM-based platforms.
Experience working with enterprise customers.
Experience using SQL, Excel, Google Sheets, or BI tools.
Background in accounting, finance, or business analysis.