Senior Data Scientist
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Cribl
Lansing, MI (In Person)
$147,500 Salary, Full-Time
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Job Description
B2B SAAS
data observability software. Cribl does differently. What does that mean? It means we are a serious company that doesn't take itself too seriously; and we're looking for people who love to get stuff done, and laugh a bit along the way. We're growing rapidly - looking for collaborative, curious, and motivated team members who are passionate about putting customers first. As a remote-first company we believe in empowering our employees to do their best work, wherever they are. As the data engine for IT and Security many of the biggest names in the most demanding industries trust Cribl to solve their most pressing data needs. Ready to do the best work of your career? Join the herd and unlock your opportunity. Why You'll Love This Role Cribl is hiring a Senior Data Scientist to join and grow our internal data science practice. In this role, you'll use predictive models and advanced analytics to give stakeholders clear foresight into what's likely to happen and what to do next. This role is embedded in our centralized data organization, reporting to our Staff Data Scientist, partnering closely with teams like Finance, GTM, and People. You'll turn rich data sets into forward-looking insights that guide planning, business strategy, and execution, and you'll contribute to the technical standards and culture that make our "Foresight First" vision a reality. We pride ourselves on fostering a collaborative and innovative culture where team members enjoy working together - whether remotely or over a meal at a foodie hot spot. If you're someone who thrives in an entrepreneurial environment and is eager to contribute to a company poised for legendary success in the tech industry, we want to hear from you! As An Active Member Of Our Team, You Will...- Own end-to-end data science initiatives that translate ambiguous business questions into clear analytical and modeling problems, from scoping through implementation.
- Build, operationalize, and continuously improve machine learning models in partnership with Data Engineering to ensure scalable training & evaluation workflows in production.
- Evaluate and interpret the performance of ML models (e.g., classification, recommendation), helping the team reason through trade-offs, limitations, and business implications of model-driven decisions.
- Conduct deep-dive analyses using advanced statistical methods to surface actionable insights to stakeholders and positively influence org-wide decision quality.
- Apply statistical modeling, experimentation, and AI-assisted analytics to expand how Cribl uses data to answer challenging questions.
- Communicate findings clearly and persuasively to executives and cross-functional stakeholders to influence strategic decision-making.
- Contribute to technical best practices and team standards, and provide mentorship to junior team members as the practice grows.
- Partner closely with Data Engineering, Analytics, and Governance to define data requirements, improve data quality, and ensure the team can efficiently generate trustworthy insights.
- We are a remote-first company and work happens across many time-zones - you may be required to occasionally perform duties outside your standard working hours. If You've Got It - We Want It
- 5+ years of experience in data science or applied statistics with a track record of driving data science projects with meaningful business impact at a software company.
- Strong background in experiment design, causal inference, predictive modeling, and ability to distinguish signal vs noise.
- Advanced SQL and Python skills, with experience working on large, messy, multi-source datasets within a modern data stack such as cloud data warehouses and transformation frameworks.
- Proven ownership of end-to-end data science and ML solutions, from problem framing and data preparation through modeling, validation, deployment to production in partnership with Data Engineering, and ongoing monitoring.
- Ability to work effectively in ambiguous, fast-changing environments, navigating from problem framing through implementation and iteration.
- Excellent communication, including the ability to present complex findings to non-technical audiences and contribute to strategic decisions
- Familiarity with AI-assisted workflows, including leveraging LLMs as tools to accelerate analysis, surface insights, or augment modeling work.
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