Senior Data Scientist
New
MexicoFull-TimeSenior
Salary75,000 - 85,000 USD per year
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Job Details
- Experience
- 3+ years
- Required Skills
- PythonSQLMachine LearningA/B testingNLPMLOps
Requirements
- 3+ years delivering data science work that shipped to production or influenced product direction.
- Experience collaborating cross-functionally and communicating with diverse stakeholders.
- Strong Python and SQL skills with production-quality coding standards (testing, reviews, documentation).
- Demonstrated mentorship and enablement skills.
- Strong applied statistics and experimentation (A/B testing, causal thinking, metric design).
- Proven ability to evaluate and improve models: dataset design, error analysis, offline/online metrics.
- Hands-on experience building with LLMs (RAG, tool calling, structured outputs, prompt iteration).
- Practical approach to LLM evaluation: golden sets, regression testing, human review loops.
- Experience with modern MLOps/LLMOps (experiment tracking, ETL, versioning, CI/CD, observability).
- NLP and information extraction/classification on social/content data.
- Experience developing and evaluating large scale Retrieval, Recommendation, and Search Systems.
Responsibilities
- Partner with Product and Engineering to identify high-impact opportunities, frame ambiguous problems, define success metrics, and choose pragmatic approaches (heuristics, statistics, ML, or GenAI).
- Lead rigorous experimentation across teams: hypothesis design, metric/guardrail definition, power analysis, A/B testing (or quasi-experiments), and clear readouts that drive decisions.
- Build and iterate on ML/AI capabilities that ship to production (e.g., classification, information extraction, ranking/recommendations, and GenAI components such as RAG or developing the agent harnesses for our core agentic journeys).
- Establish best-in-class evaluation practices for both ML and LLM features: golden datasets, offline/online evaluation plans, regression suites, and monitoring.
- Enable engineers to build safely and effectively with AI by coaching on prompt patterns, tool/function calling, structured outputs, guardrails, and debugging/evaluation workflows.
- Design and support agentic workflows with clear constraints, observability, and fallbacks.
- Support the end-to-end lifecycle of deployed models and AI systems: data requirements, training/fine-tuning, validation, deployment, monitoring, and incident response.
- Raise org-wide leverage by creating reusable assets and running enablement workshops.
- Communicate insights and tradeoffs clearly to technical and non-technical stakeholders.
- Champion responsible, privacy-aware AI, bias/fairness considerations, and human-in-the-loop workflows.
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