Description About the Role As a Senior Data Scientist at NinjaOne, you will play a critical role in accelerating business growth by applying advanced analytics and machine learning. You'll work at the intersection of data, product, and go-to-market teams to build predictive models, experimentation frameworks, and actionable insights that directly influence revenue, customer acquisition, retention, and operational efficiency. This role offers the opportunity to own high-impact initiatives end-to-end. Drive problem framing, model development, production deployment and business adoption while working on large-scale, real-world data in a fast-growing, product-led organization. If you're motivated by solving complex problems, shipping production ML, and seeing your work drive measurable business outcomes, this is a chance to make a massive impact at scale. Location - We are flexible on remote working from home, if you are located in the USA and reside in one of the following states - CA, CO, CT, FL, GA, *IL, KS, MA, MD, ME, NJ, NC, NY, OR, TN, TX, VA, and WA. We have physical offices in Austin, TX and Tampa, FL, if you prefer a hybrid option. We hire the best software engineers, but experience in our stack can't hurt: NinjaOne is built on Java , Kotlin, C++, Golang and Postgres ; supporting millions of user endpoints and running as a scalable cloud service in AWS . Knowing large-scale datastore bottlenecks, asynchronous application design and client-server architecture will help you. What You'll be Doing Design, build, and deploy advanced analytical models to drive insights and decision-making across sales, marketing, customer support, and product teams. Develop predictive models for forecasting, churn, lead scoring, pipeline health, customer lifetime value, and usage-based segmentation. Partner closely with stakeholders to translate business questions into data science problems, metrics, and experimentation strategies. Build scalable data pipelines and feature engineering workflows to support model training, inference, and real-time analytics. Create experimentation frameworks and run A/B tests to optimize marketing campaigns, product features, and support workflows. Implement causal inference and attribution models to measure campaign effectiveness and product adoption drivers. Lead data quality, validation, and governance efforts to ensure high trust in analytical outputs. Mentor junior data scientists and analysts, establishing best practices for modeling, experimentation, and documentation. Collaborate with engineering and operations teams to productionize models and embed intelligence into core business systems. Other duties as needed About You Bachelor's or Master's degree in Computer Science, Statistics, Engineering, or a related quantitative field. 10+ years of experience applying data science, machine learning, and advanced analytics to business and product problems. Expert proficiency in Python and SQL for data analysis, modeling, and production ML workflows. Strong foundation in statistical modeling, machine learning, experimentation, and causal inference. Hands-on experience deploying and operating production ML systems in cloud environments (AWS, Azure), including MLOps best practices. Experience that will make you a standout candidate: Previous experience working with large-scale data pipelines and machine learning models. Mastery of Generative AI and Deep Learning frameworks. About Us NinjaOne unifies IT to simplify work for more than 35,000 customers in 140+ countries. The NinjaOne Unified IT Operations Platform delivers endpoint management, autonomous patching, backup, and remote access in a single console to improve efficiency, increase resilience, and reduce spend. By automating IT and managing all endpoints, organizations give employees a great technology experience at work. NinjaOne is obsessed with customer success and has retained a 98% customer satisfaction score for more than 5 years. What You'll Love We are a collaborative, kind, and curious community. We honor your flexibility needs with full-time work that is hybrid remote. We have you covered with our comprehensive benefits package, which includes medical, dental, and vision insurance. We help you prepare for your financial future with our 401(k) plan. We prioritize your work-life balance with our unlimited PTO. We reward your work with opportunity for growth and advancement. Additional Information This position is NOT eligible for Visa sponsorship. *Due to operational policies, NinjaOne is unable to hire for this role within the city limits of Chicago. We will consider all qualified candidates who reside outside of the city proper or are willing to self-relocate. Starting pay for the successful applicant depends on a variety of job-related factors, including but not limited to location, market demands, experience, job-related knowledge, and skills. The benefits available for this position include medical, dental, vision, 401(k) plan, life insurance coverage and PTO. For roles based in California, Colorado, Maryland, New Jersey, or Washington the base salary hiring range for this position is $140,000 - $200,000 per year. For roles based in New York, the base salary hiring range for this position is $140,000 - $200,000 per year. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, genetic information, marital status, veteran status, or any other status protected by applicable law. We are committed to providing an inclusive and diverse work environment. #LI-KS2 #LI-Remote #LI-Hybrid #BI-Remote

• Translate medical policy into executable logic - Read and interpret medical policies and clinical criteria (e.g., lab thresholds, temporal windows, trend logic, exclusions). • Convert requirements into correct, maintainable SQL and Python implementations (e.g., creatinine-based AKI rules, bilirubin thresholds, troponin dynamics, ABG-derived criteria). • Design rule representations that are composable and auditable (clear inputs, outputs, assumptions, edge cases). • Prompt engineering and system parameter tuning for AI configuration that extracts clinical information from medical records. • Build robust clinical feature pipelines. • Create and maintain pipelines that compute clinical features from extracted signals (labs, vitals, flowsheets, notes-derived facts). • Handle tricky realities: missing timestamps, multiple measurement sources, unit normalization, deduplication, conflicting values, provenance tracking. • Own measurement, evaluation, and continuous quality improvement - Define and instrument accuracy metrics for the AI system that extracts data from medical records. • Build gold datasets, sampling strategies, and review workflows with clinical/operations partners. • Perform error analysis, identify root causes (retrieval failures, OCR issues, extraction ambiguity, policy interpretation gaps), and drive improvements. • Establish engineering frameworks and tooling - Create reusable tooling for policy-to-code translation: templates, test harnesses, validation suites, regression checks, and monitoring dashboards. • Improve infrastructure for large-scale runs: orchestration, logging, lineage, versioning, and reproducibility. • Implement guardrails and QA gates so policy logic changes are safe, traceable, and measurable. • Partner deeply with domain experts - Work with clinicians, policy specialists, and operations to clarify ambiguous requirements and ensure implementations reflect real-world intent. • Produce clear documentation that explains what the code is doing and why, with examples and edge-case handling.

• Provide direction and guidance to a team of Senior and Staff Data Scientists, enabling them to do their best work • Collaborate with the leadership team to define key technical and business metrics and objectives • Translate objectives into internal team priorities and assignments • Drive sprints and work with cross-functional stakeholders to appropriately prioritize various initiatives to improve customer metrics • Hire, mentor and develop team members • Foster a culture of innovation, collaboration, and continuous improvement • Communicate technical concepts and strategies to technical and non-technical stakeholders effectively • Own the success of various models on the field by continuously monitoring KPI’s and initiating projects to improve quality.

• Master understanding of various claims payment policies and healthcare concepts. • Build and improve the models by thoughtfully curating data and creating features. • Precisely measure and improve model performance against real-world outcomes. Make operational recommendations to the customer to optimize model results. • Interpret and refine large-scale data created by complex business workflows. • Advance the team’s capabilities by improving pipelines, infrastructure, and tools.