Data Scientist at sciencelogic

Department: Product Engineering

Compensation

$140,000 – $165,000/yr

Setup: Remote
Type: Full-time
Level: mid
Posted: Aug 12, 2026

Description

ScienceLogic is hiring a Data Scientist to evaluate, benchmark, and improve a suite of small, locally-hosted language models and agentic systems. The role focuses on LLM evaluation harnesses, response-quality metrics, red-teaming, retrieval and agent-trajectory analysis, production behavioral regression, and predictive trend modeling from operational telemetry. The Data Scientist will also analyze AIOps and IT operations data, quantify system economics, and communicate findings to engineering and product stakeholders. The position requires a bachelor's or master's degree in a relevant field, at least three years of data science or applied quantitative analysis experience, strong Python and SQL skills, and experience with LLM evaluation and production predictive models.

Required Skills

Applied statistics
Python
SQL
LLM evaluation
Time-series modeling
Retrieval evaluation
Red-teaming
Anomaly detection

Additional Information

Minimum Education: Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, or a related field, or equivalent experience
Required Previous Experiences: At least 3 years of experience in data science, machine learning, or applied quantitative analysis
Experience building, deploying, and monitoring production predictive or time-series models, including forecasting, anomaly detection, or trend analysis
Experience evaluating, analyzing, or improving LLM or NLP systems
Hands-on experience with foundation models and modern LLM evaluation and tooling
Preferred Qualifications: Experience optimizing small or self-hosted models under compute, memory, or latency constraints
Experience with retrieval-augmented systems and large-scale retrieval evaluation
Experience with agentic frameworks and tool-use orchestration analysis
Familiarity with LLM red-teaming or adversarial robustness
Domain background in IT operations, AIOps, NOC, ITSM, observability, or log and telemetry anomaly detection
Experience with large-scale analytical and big-data stores
Cloud experience for data science and machine learning workloads
Exposure to enterprise security and compliance constraints