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Qode

Contract Data/ML Engineer-Scoring Reliability & Candidate Archetypes (Part-time)

part-time Remote

AI overview

Own the implementation of analytics features for candidate assessment, focusing on data modeling, clustering, and developing reliable reporting mechanisms.

Job: Contract Data/ML Engineer — Scoring Reliability & Candidate Archetypes - ASAP

Job Type: Part-time (for a 100-hour project)
Job Presence: Remote (optional for Onsite and Hybrid in Vietnam)
Candidate Location: Vietnam, India
Joining Date: ASAP as we're hiring this role urgently

Summary

Own the end-to-end implementation of two analytics features in Qode’s multi-agent assessment stack: (1) bootstrap confidence intervals (CIs) for per-question scores to communicate stability/disagreement across evaluators, and (2) candidate archetype discovery via clustering to surface talent patterns beyond raw scores. You’ll ship data plumbing, models, integrations, and lightweight reporting.

What you’ll do

  • Data foundations: ensure per-candidate, per-question, per-agent criterion scores are structured and queryable; add/modify tables and JSON schemas as needed.
  • Bootstrap CIs: implement agent-level resampling, compute CI-90/CI-95, derive stability labels (high/medium/low), and persist alongside normalized scores; batch backfill existing records.
  • Archetypes: build standardized candidate feature vectors (per-question and/or per-criterion), run clustering (K-means/GMM/hierarchical), evaluate (e.g., silhouette), and generate human-readable labels from centroids and summaries.
  • Integrations: expose CI fields and cluster IDs/labels via API and internal dashboards; add basic charts/UX to surface stability and “candidate type.”
  • Reliability & performance: write unit/integration tests, guardrails (min N agents), and ensure pipeline runtime stays within agreed budgets.
  • Docs & handoff: clear README/runbooks covering data contracts, thresholds, and ops.

Must-have skills and qualifications

  • 3-5 years of experience in a relevant role
  • Python (pandas, NumPy, scikit-learn), SQL, DB migrations (e.g., Postgres).
  • Statistical resampling (bootstrap), clustering, model selection/validation.
  • Data engineering for batch jobs/backfills; API integration.
  • Pragmatic product sense for labeling clusters and communicating uncertainty.


Nice-to-haves

  • Airflow/dbt/Prefect; Grafana/Metabase; experience with multi-agent/LLM evaluation pipelines; cloud (GCP/AWS/Azure); Docker/Kubernetes.


Deliverables & acceptance criteria

  • CI service/module + persisted mean, ci_low, ci_high, stability_label for 100% of scored candidate-question rows with ≥N agents; reproducible backfill completed.
  • Clustering job that assigns cluster_id and cluster_label to each candidate; labels documented with centroid profiles and example candidates.
  • API fields and minimal dashboard tiles (score±CI, stability badge; “Candidate Type” with top strengths/weaknesses).
  • Tests (unit + E2E), monitoring hooks, and runbooks.
Qode

Qode is dedicated to helping technical talent around the world find meaningful careers that match their skills and interests. Our platform provides a range of resources and tools that empower job seekers to take control of their careers and connect with top employers across a variety of industries. We believe that every individual deserves to find work that they're passionate about, and we are committed to making that vision a reality.Qode's team of experienced professionals is passionate about creating a better world of work by providing innovative solutions that improve the job search process for both job seekers and employers. We believe in transparency, trust, and collaboration, and we strive to build strong relationships with our customers and partners. Through our platform, we aim to create a more engaged and fulfilled global workforce that drives innovation and growth.

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