Data Scientist

Summary

ScienceLogic is redefining IT operations for the modern enterprise. They are seeking a strong Data Scientist to join their growing Data Science team, responsible for optimizing language models and ensuring high-quality outcomes through evaluation and predictive modeling.

Responsibilities

• Design and own evaluation harnesses for LLM and agentic outputs — golden sets, regression suites, and rubric-based scoring
• Build and calibrate LLM-as-judge pipelines; validate judges against human labels and control for their bias and variance
• Define and track response-quality metrics: faithfulness/groundedness, hallucination rate, answer relevance and completeness, instruction-following, and persona adherence
• Curate, version, and grow evaluation datasets as the product and its surfaces evolve
• Benchmark the models in the suite against each other to decide which model handles which task, and quantify the quality cost of running smaller, local models versus larger alternatives
• Red-team the system: prompt injection, jailbreaks, tool-misuse, and edge-case discovery
• Design chaos and stress tests that probe model and agent reliability under degraded or hostile conditions
• Characterize failure modes and feed them back into guardrails and regression coverage
• Evaluate retrieval quality over the document corpus — recall@k, MRR/nDCG, context precision and recall — and run experiments on chunking, indexing, and hybrid retrieval strategies
• Analyze multi-step agent trajectories: tool-call correctness, trajectory efficiency, replayable-state inspection, and guardrail-breach behavior
• Assess intent classification and routing quality as measurable components, not black boxes
• Build standing evaluation that catches quality and behavioral regressions when a model in the suite is swapped, upgraded, or re-quantized, or when prompts and pipelines change
• Monitor output-distribution and quality drift in production; distinguish genuine regressions from noise on stochastic outputs
• Recommend and validate fixes through the levers available with local models — prompt changes, retrieval and grounding adjustments, routing changes, or model selection
• Build, ship, and own production models that forecast and surface trends from operational telemetry — capacity and resource forecasting, anomaly prediction, and early-warning signals on metrics and logs
• Take these from prototype to production and keep them healthy: deployment, monitoring, recalibration, and retraining as data and behavior shift
• Define accuracy and lead-time metrics that matter operationally — precision/recall on predicted incidents, forecast error, how far ahead a signal fires — not just offline scores
• Wire predictive signals into the LLM and agentic layer so forecasts and trends feed reasoning, advisories, and operator-facing recommendations
• Apply AIOps/NOC analysis where it's the product: log anomaly detection, event correlation, and root-cause and problem analysis
• Quantify the economics of the system — cost and token consumption per interaction, interaction-type taxonomies — and connect them to customer-facing value metrics like MTTR and operator-hours
• Communicate findings to engineering and product stakeholders through clear, in-context analysis
• Use LLM-assisted workflows to scale the work itself — drafting analyses, generating synthetic evaluation cases, and bootstrapping labeled data for human refinement
• Track and adopt state-of-the-art evaluation, retrieval, and agentic-analysis techniques; bring the useful ones into the team's workflow

Skills

• Bachelor's or Master's in Data Science, Computer Science, Statistics, Mathematics, or a related field or equivalent experience
• 3+ years in data science, ML, or applied quantitative analysis
• Strong applied statistics, with the judgment to design sound experiments and significance tests on noisy, non-deterministic outputs (not just clean A/B conversion)
• Experience building, deploying, and monitoring predictive or time-series models in production: forecasting, anomaly detection, or trend analysis, including recalibration as data shifts
• Demonstrated work evaluating, analyzing, or improving LLM or NLP systems: eval design, quality measurement, retrieval evaluation, or agent analysis
• Proficiency in Python
• Strong SQL and comfort querying large analytical datasets
• Fluency with foundation models and hands-on experience with the modern LLM evaluation and tooling layer — eval/harness frameworks, judge pipelines, and the libraries used to serve, prompt, and test models
• Ability to build analysis and visualization in code
• Experience getting strong results out of small or self-hosted/local models under compute, memory, or latency constraints — quantization-aware evaluation, prompt and context optimization, or model routing
• Experience with retrieval-augmented systems and retrieval evaluation at scale
• Experience with agentic frameworks and tool-use/orchestration analysis, including human-in-the-loop and replayable-state patterns
• Familiarity with red-teaming or adversarial robustness for LLMs
• Domain background in IT operations — AIOps, NOC, ITSM, observability, or anomaly detection on logs and telemetry
• Experience with large-scale analytical and big-data stores
• Cloud experience for data science and ML workloads
• Exposure to enterprise security and compliance constraints in a delivery context

Qualifications

Must Haves

• Bachelor's or Master's in Data Science, Computer Science, Statistics, Mathematics, or a related field or equivalent experience
• 3+ years in data science, ML, or applied quantitative analysis
• Strong applied statistics, with the judgment to design sound experiments and significance tests on noisy, non-deterministic outputs (not just clean A/B conversion)
• Experience building, deploying, and monitoring predictive or time-series models in production: forecasting, anomaly detection, or trend analysis, including recalibration as data shifts
• Demonstrated work evaluating, analyzing, or improving LLM or NLP systems: eval design, quality measurement, retrieval evaluation, or agent analysis
• Proficiency in Python
• Strong SQL and comfort querying large analytical datasets
• Fluency with foundation models and hands-on experience with the modern LLM evaluation and tooling layer — eval/harness frameworks, judge pipelines, and the libraries used to serve, prompt, and test models
• Ability to build analysis and visualization in code

Nice to Haves

• Experience getting strong results out of small or self-hosted/local models under compute, memory, or latency constraints — quantization-aware evaluation, prompt and context optimization, or model routing
• Experience with retrieval-augmented systems and retrieval evaluation at scale
• Experience with agentic frameworks and tool-use/orchestration analysis, including human-in-the-loop and replayable-state patterns
• Familiarity with red-teaming or adversarial robustness for LLMs
• Domain background in IT operations — AIOps, NOC, ITSM, observability, or anomaly detection on logs and telemetry
• Experience with large-scale analytical and big-data stores
• Cloud experience for data science and ML workloads
• Exposure to enterprise security and compliance constraints in a delivery context

Benefits

• Comprehensive medical, dental and vision plans.
• 401(k) plan with employer match.
• Flexible Paid Time Off (FTO) so that you can take the time that you need to re-energize.
• Volunteer Time Off (VTO) - take two days off per calendar year to volunteer with your preferred charitable organization.
• 5-year Service Milestone Sabbatical.
• Paid parental leave.
• Generous employee referral bonus program.
• Pet insurance.
• HQ Office centrally located in Reston Town Center featuring a well-stocked kitchen with rotating snacks and beverages, and catered lunch on Thursdays.
• Regular virtual company-wide events, including cooking classes, yoga, meditation and more.
• The opportunity to learn and develop from some of the best and brightest minds in the industry!