Data Analyst

As our Data Analyst – Labeling, you will be the analytical heartbeat of our Data Labeling Team. You’ll design the dashboards, pipelines, and reporting layers that reveal performance, identify friction, and enable high-precision decisions. Your work will help transform how we measure and scale labeling operations — influencing ML performance, customer delivery, and internal alignment. You will report to the Head of Live Services and serve as the dedicated analytics partner for labeling leadership.

What You’ll Own & Drive

• Build visibility: Design dashboards and reporting tools to track labeling throughput, SLA compliance, backlog dynamics, reviewer performance, and QA patterns.

• Define new metrics: Propose and implement creative ways to measure labeling quality, accuracy, efficiency, and cost across workflows.

• Support stakeholders: Provide analytical support to ML Engineering (e.g., model-label performance), Product (labeling impact on features), and Sales/CS (client deliverables and operational SLAs).

• Model labeling effort: Quantify task complexity, labeling speed, and reviewer dynamics across tools and task types.

• Own the data foundation: Build pipelines that consolidate data from SQL (Redshift, Postgres), annotation tools, QA audits, and semi-structured feedback into a coherent source of truth.

• Scale trust in data: Help the labeling org move from intuition to instrumentation by making operational data accessible and reliable.

The Qualities That Set You Apart

• Analytical precision – You turn operational noise into clarity using logic, structure, and well-formed hypotheses.

• Builder mindset – You bring structure to ambiguity and enjoy being the first to formalize systems.

• Cross-functional fluency – You can speak with ML, Ops, Product, or Sales and deliver insights in a language they understand.

• Operational curiosity – You care about effort, feedback loops, and improving the system — not just answering questions.

• Trustworthy and self-driven – You operate independently and with integrity, always raising the bar for analytical rigor.

Your Background

• 4–7 years of experience as a data analyst, business analyst, or ops analyst (preferably in tech, fintech, or ML/ops settings).

• Strong SQL skills (Redshift preferred), with experience in joining large datasets and building reproducible queries.

• Proficiency in dashboarding tools like Tableau, Metabase, or Looker.

• Working knowledge of Python for automation or data exploration.

• Experience with labeling systems, QA pipelines, or operations metrics is a plus.

• Proven success owning end-to-end analytics cycles (from question to insight to impact).

• Comfortable translating complex systems into understandable views for diverse audiences.