Data Scientist
Role details
Job location
Tech stack
Job description
- Exceptional verbal and written skills to convey ideas, problems and solutions
- Establish trust and confidence as the data scientist, up, across, and down the organization
- Design, build, and deploy ML models across structured and unstructured data
- Translate ambiguous business problems into well-scoped analytical solutions with clear trade-offs documented
- Write production-grade Python and SQL; contribute to shared codebases with reproducibility and refactorability in mind
- Collaborate with data engineering on pipeline architecture, feature stores, and model deployment patterns
- Leverage LLMs and modern AI tooling where appropriate, sound judgment on when not to
- Mentor analysts and junior data scientists through code review, whiteboarding, and hands-on pairing
- Own model documentation, versioning, and knowledge artifacts (Confluence, GitHub)
- Monitor deployed models for drift and degradation; refresh proactively, not reactively
Requirements
Do you have experience in Communication skills?, Required:
- Bachelor's/Master's in a STEM field (statistics, economics, CS, engineering, applied math, or similar)
- Expert in SQL and Python in real-world
Preferred:
- Ph.D. in a quantitative field a plus, equivalent experience considered: 5+ years in a production data science role
- Alternative to education, 6+ years of experience as contributor/leader
- Demonstrated track record shipping models that drove measurable business outcomes
Skills & Abilities
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Strong verbal communication skills: ability to effectively communicate cross-functionally
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Expert-level SQL and Python in production settings
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Strong applied statistics - comfortable in frequentist and Bayesian frameworks
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Experience with the modern ML stack: scikit-learn, XGBoost, PyTorch or equivalent; experiment tracking (MLflow, W&B, or similar)
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MLOps fundamentals: versioning, model registries, scheduled retraining, monitoring
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Git-based workflows (GitHub or GitLab) with code review habits
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Familiarity with dbt or orchestration tools (Airflow, Prefect, etc.)
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Exposure to web behavioral data and customer lifecycle modeling (churn, propensity, LTV)