--- title: 'Data Scientist at ScienceLogic' canonical: 'https://feeny.ai/job/data-scientist-sciencelogic-united-states-016ctwvv7r90' type: 'job' last_seen: '2026-09-09' --- # Data Scientist at ScienceLogic - **Company:** ScienceLogic - **Location:** United States - **Employment:** full-time - **Work type:** remote - **Posted:** 2026-08-12 - **Last confirmed live:** 2026-09-09 - **Apply:** https://jobs.ashbyhq.com/sciencelogic/80ad538a-5afb-48d7-ae97-83455ad2aa93 ## Job description ## ABOUT SCIENCELOGIC… ScienceLogic is redefining IT operations for the modern enterprise. Our AIOps platform empowers organizations to achieve Autonomic IT — where systems are self-healing, self-optimizing, and seamlessly aligned with business outcomes. We help enterprises and service providers gain unified visibility across hybrid and multi-cloud environments, automate workflows, and unlock performance at scale. We’re accelerating digital transformation through the power of automation, AI, and analytics — giving IT and business leaders the tools to deliver superior customer experiences, drive efficiency, and innovate with confidence. We're looking for a strong Data Scientist to join our growing Data Science team. We run a suite of small, locally-hosted language models in production — not a single frontier API. That deliberate architecture defines this role: each model is more constrained than a giant hosted one, so product quality comes from how well we evaluate, route, prompt, ground, and orchestrate the models we have. Your job is to get the best possible outcomes out of that suite. This is not classical predictive modeling. The object of measurement is the LLM system itself — its answers, retrieval, multi-step agent behavior, and reliability under adversarial and edge-case conditions. You'll define what "good" means for a non-deterministic system running on bounded local models, build the evaluation infrastructure that catches regressions, and turn interaction data into the analysis that tells engineering and product where to invest. You'll also build production prediction and trend capability — forecasting, anomaly detection, and early-warning signals over operational telemetry — that feeds directly into that system. You'll work across data scientists, ML/inference engineers, frontend, and product in an enterprise environment with real security and compliance constraints. If you think in eval suites, failure modes, and groundedness — and you're energized by squeezing reliable, high-quality behavior out of small models under real resource budgets — this is the role. ## Key Responsibilities Evaluation & Response Quality - 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. Adversarial & Robustness Testing - 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. Retrieval & Agentic Trajectory Analysis - 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. Behavioral Regression & Drift - 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. Predictive & Trend Modeling - 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. Domain & Value Analytics - 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. Method & Innovation - 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. Required Experience - Bachelor's or Master's in Data Science, Computer Science, Statistics, Mathematics, or a related field or equivalent experience. - US Citizen or Green Card Holder - 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. ## Preferred Qualifications - 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 & PERKS - 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! Don’t meet every single requirement? Studies have shown that women and people of color are less likely to apply to jobs unless they meet every single qualification. At ScienceLogic, we are dedicated to building a diverse, inclusive and authentic workplace, so if you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, or any other applicable legally protected characteristics in the location in which you are applying. ## ABOUT SCIENCELOGIC ScienceLogic is a leader in IT Operations Management, providing modern IT operations with actionable insights to resolve and predict problems faster in a digital, ephemeral world. Its solution sees everything across cloud and distributed architectures, contextualizes data through relationship mapping, and acts on this insight through integration and automation. www.sciencelogic.com http://www.sciencelogic.com ## About ScienceLogic ## Company Overview - **One-liner**: ScienceLogic delivers an agentic AI engine for IT Operations (ITOM and AIOps) that unites observability, AI, and automation into a single platform to anticipate, prevent, and resolve IT issues. - **Entity Type**: Private (Funding stage: Series E / Debt Financing; last raised $105M in a Series E round in February 2021) - **Headquarters**: Reston, Virginia, United States - **Founded**: 2003 - **Founders**: Dave Link (CEO), Richard Chart (Chief Scientist & Co-Founder) ## Core Business - **Primary industry**: IT Operations Management (ITOM), AIOps, and Observability Software - **Target customers**: B2B – Enterprise IT, Service Providers, and Public Sector agencies worldwide - **Mission**: To make IT intelligent, resilient, and transformative — helping organizations move from fragmented, reactive operations to intelligent, coordinated IT that delivers reliable services and frees teams to innovate. ## Products & Services - **ScienceLogic Platform**: A unified, agentic AI engine that provides full-stack observability across hybrid environments (on-premises, cloud, and edge). - **Skylar One**: A simplified UI delivering real-time visibility across hybrid IT infrastructure, consolidating tools and data. - **Skylar AI**: The agentic AI layer that observes, advises, and acts — providing automated root-cause analysis and intelligent recommendations (Skylar Advisor). - **Skylar Automation**: Low-code/no-code workflow automation to turn alerts into action, automate remediation, and improve MTTR. - **Skylar Compliance**: Monitors, records, and backs up network configurations to simplify compliance audits and speed restoration. **Product Type**: SaaS (deployable in hours) ## Market Standing - **Valuation/Market Cap**: Not publicly disclosed (private company) - **Key Metric**: **Annual Revenue: $121.2M** (estimated, third-party source); **Total Funding: $235.2M** across 6 rounds - **Notable Investors/Partners**: Investors include **Silver Lake**, **Mizuho Financial Group**, **Sutter Hill Ventures**, **Grotech Ventures**, and **New England Pension Fund** (via most recent Series E). Key partners include major cloud and IT ecosystem players (AWS, Dell, VMware, Cisco, IBM). - **Growth Signals**: - Named a **Leader in the Forrester Wave™: AIOps Platforms** (highest score in Strategy) - Recognized as a **Visionary in the 2025 Gartner® Magic Quadrant™ for Observability Platforms** - **157% average ROI** per commissioned Total Economic Impact™ study (Forrester) - **20+ years** of innovation in the IT operations space - Operating in **14 countries** (including US, India, UK, Taiwan, Australia, Ireland, Netherlands) - **33,600+ LinkedIn followers** with steady monthly growth (+0.6%) - Acquired **AppFirst (2016)** and **Restorepoint** to expand capabilities ## Competitive Advantages - **Patented Discovery**: Proprietary discovery techniques provide deep visibility across all technologies and vendors — on-premises, cloud, and everything in between — regardless of stack. - **Agentic AI Focus**: Skylar AI is positioned as an autonomous "agentic" layer that doesn't just alert but *acts*, setting them apart from legacy monitoring tools. - **FedRAMP Moderate Authorization**: Authorized for U.S. public sector workloads — a significant trust and compliance moat. - **Breadth of Integrations**: Extensive library of pre-built integrations with 50+ major tools (Cisco, AWS, Dell, VMware, ServiceNow, Splunk, etc.). - **Customer Satisfaction**: Top Rated by customers on **TrustRadius for 7 consecutive years**; **4.4/5 rating** on Gartner Peer Insights. ## Strategic Focus - **Agentic AI & Automation**: Doubling down on AI-driven autonomous operations to reduce human toil and incident response time. - **Hybrid Cloud Visibility**: Deepening observability across hybrid and multi-cloud environments, especially targeting enterprises with complex legacy + cloud estates. - **Public Sector Expansion**: Leveraging FedRAMP authorization to grow in government and defense. - **Low-Code/No-Code Automation**: Expanding Skylar Automation to make IT process automation accessible to non-programmers. - **SaaS-first Delivery**: Accelerating cloud-native SaaS delivery to reduce deployment times from weeks to hours. ## Why Work Here - **Culture & Values**: Strong emphasis on "Customer Obsessed," "Innovate Together," "You Matter," "Empower Success," and "Be Bold." Multiple Employee Resource Groups (ERGs): **PowHer** (women), **SciLo Pride** (LGBTQIA+), **SciLo Vets** (veterans), **Spectrum** (neurodiversity), and **Black Logicians**. - **Work Environment**: Highly collaborative, agile work style. Remote/hybrid policy not explicitly stated, but based on global team presence (14 countries) and SaaS nature, flexible/remote options are likely available. Headquarters in Reston, VA with major hubs in India, UK, and other countries. - **Engineering-Led**: VP of Engineering, Skylar AI is a key role — strong focus on innovation and AI. Engineers get to work on cutting-edge agentic AI and large-scale observability. - **Impact**: Employees work on "solving some of the world’s biggest IT problems" for enterprises and governments. - **Notable Perks**: Equal Opportunity Employer, E-Verify participant. Transparency in Coverage compliance. Reasonable accommodations provided. - **Headcount**: ~430 employees (with a technical team of 208+). Key talent comes from **AWS, ServiceNow, Splunk, Cisco, IBM, Deloitte, and Datadog** — indicating a high-caliber peer group. - **Active Job Postings**: 13 open roles (as of mid-2026) including Sr. Systems Engineer, Sr. Engineer Test, Account Executive, and VP of Engineering, Skylar AI. ## Sources 1. [sciencelogic.com](https://sciencelogic.com/about/company) 2. [sciencelogic.com](https://sciencelogic.com/) 3. [linkedin.com](https://www.linkedin.com/company/sciencelogic) 4. [sciencelogic.com](https://sciencelogic.com/about/careers) 5. 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