[Remote] Data Scientist

Note: The job is a remote job and is open to candidates in USA. Jobber is a company dedicated to empowering small businesses with technology solutions. They are seeking a Data Scientist to ensure the quality and reliability of their machine learning models, focusing on validation, monitoring, and collaboration with various teams to enhance model performance.

Responsibilities

• Design, implement, and maintain ML model validation frameworks, including custom evaluation metrics, loss functions, and statistical tests, to ensure model quality before and after deployment
• Build and own regression test suites for ML and LLM models, catching performance regressions and unexpected behaviour across model updates and data drift scenarios
• Develop and execute MCP evaluations, systematically assessing model capabilities, edge cases, and failure modes across relevant business contexts
• Monitor models in production using statistical process control, drift detection, and alerting pipelines; proactively surface issues before they impact customers
• Collaborate with senior data scientists to contribute to the design and refinement of ML model architectures, offering feedback grounded in validation results
• Document evaluation methodologies, test results, and monitoring runbooks clearly enough that stakeholders across technical and business teams can understand model health
• Stay current with advancements in LLM evaluation techniques, AI safety, and model observability, and apply emerging best practices to our workflows
• Communicate findings clearly and concisely to stakeholders, translating model performance signals into actionable recommendations

Skills

• Industry experience in data science, machine learning, or a closely related quantitative field
• Proficiency in Python and the core DS stack: Pandas, Scikit-Learn, XGBoost, and at least one deep learning framework (PyTorch or TensorFlow)
• Solid grasp of statistical concepts underpinning model evaluation: bias–variance tradeoff, calibration, confidence intervals, A/B testing, and data drift
• Experience with LLM evaluation frameworks (e.g. RAGAS, Eleuther AI Eval Harness, or custom LLM eval pipelines)
• Hands-on experience designing custom evaluation metrics; you've gone beyond off-the-shelf metrics when the problem demanded it
• Strong understanding of ML and LLM model architectures — you can reason about how a model is built and why it behaves the way it does
• High proficiency in SQL for data exploration, feature validation, and debugging model inputs
• Exceptional attention to detail — you treat model validation with the same rigour as software QA
• Strong written and verbal communication skills; comfortable presenting findings to both technical peers and non-technical stakeholders
• Experience building model evaluation or monitoring solutions within a Snowflake environment, e.g. logging predictions and ground truth to Snowflake tables, computing eval metrics via Snowpark, or building monitoring dashboards on top of Snowflake data
• Familiarity with Snowpark (Python) for running data transformations and ML workflows directly within Snowflake
• Exposure to model regression testing patterns in a CI/CD context (e.g. running evals on every PR)
• Familiarity with prompt engineering and evaluation strategies for LLM-powered features
• Experience working in a SaaS environment and an appreciation for how model quality translates to customer impact

Benefits

• Equity rewards
• Annual stipends for health and wellness
• Retirement savings matching
• An extended health package with fully paid premiums for body and mind
• Access to a dedicated talent development program that includes career coaching and opportunities for career development
• Extended health benefits package with fully paid premiums for both body and mind
• Matching in RRSP, TFSA or FHSA
• Stock options
• A dedicated Talent Development team and access to coaching, learning, and leadership programs to help you grow your career, reach your goals, and unlock your full potential

Company Overview

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