NoGood

Data Scientist

NoGood

full-time

Posted on:

Location Type: Hybrid

Location: Cairo • Egypt

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Tech Stack

Open SourcePythonSQL

About the role

  • Work with large datasets. Own efficient querying, cleaning, labeling, and taxonomy alignment for brands, SKUs, and categories.
  • Design sampling and classification strategies that turn noisy LLM outputs and crawler logs into reliable brand and product insights.
  • Use LLMs and NLP to extract structure from unstructured text at scale. Topics include query fan-out, sentiment, citation extraction, and entity linking for brands, products, and creators.
  • Define product-grade metrics. Create durable definitions for visibility score, answer coverage, product presence, and agentic checkout readiness.
  • Build and run experimentation frameworks. A/B tests, holdouts, counterfactuals, and uplift modeling to quantify impact on citations, share of voice, and conversions.
  • Develop and refine predictive models that analyze and forecast AI search behavior across models and surfaces.
  • Translate complex findings into clear decisions. Partner with the founding team to inform roadmap, pricing, and customer playbooks.
  • Create evaluation harnesses. Establish automatic evals and human-in-the-loop labeling for model quality, bias, and drift across LLM providers.
  • Detect anomalies. Build monitors for crawler behavior, rankings, and feed health to catch regressions before customers do.

Requirements

  • 3 to 7 years in applied analytics or data science within tech, marketing, or ads. Startup or high-growth experience preferred.
  • Strong Python and SQL. Comfortable in notebooks and in code reviews.
  • Skilled with sampling and inference. Stratified sampling, bootstrapping, extrapolation, reweighting, and variance estimation.
  • Solid ML toolkit. Time series, classification, regression, weak supervision, and methods to estimate event frequency from partial observations.
  • Practical LLM knowledge. Strengths in prompt design, structured extraction, embeddings, and an understanding of model limits and failure modes.
  • Curious and current on multi-modal and LLM research. You enjoy reading papers and pressure testing ideas in real data.
  • Builder mindset in a fast team. You value clarity, speed, and ownership.
  • Nice to have:
  • - Experience with large-scale information extraction or search quality
  • - Background in causal inference, MMM, or attribution models
  • - Hands-on work with product feeds and retail catalogs
  • - Contributions to open source or published work we can read
  • - Deployed side projects we can click through.
Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools
PythonSQLsamplinginferencetime seriesclassificationregressionweak supervisionLLMpredictive modeling
Soft Skills
curiosityclarityspeedownershipcommunication