Senior Data Scientist

Overview:

Business Data Science can unlock transformative potential in every part of our business. We aim to empower people and systems to make smarter, faster, and more meaningful decisions—so we cannot just adapt to the future but shape it. At SolarWinds, we apply AI and machine learning as tools for solving real business challenges, operating with integrity, transparency, and accountability in how data is used, and models are deployed. By collaborating cross-functionally, we ensure our technology serves people first, not the other way around. We’re building a high-performing AI & ML team that develops intelligent systems to optimize operations and uncover actionable insights that drive strategic decisions. Our culture is rooted in intelligent transformation—where innovation is propelled by the purposeful use of data and technology, elevating how our business thinks, operates, and serves.

As a Data Scientist at SolarWinds, you will join a collaborative, innovative team driving business transformation through data. You’ll influence decisions by primarily delivering insights with machine learning, intelligence, and secondarily shaping and building generative AI solutions, participating in both independent analysis and team-driven projects. The role involves identifying opportunities, building and deploying models, performing thorough analyses, and communicating findings to stakeholders across the organization. You’ll apply advanced analytics—regression, clustering, deep learning, NLP—and support solutions like Azure-hosted RAG LLMs, as well as shaping and influencing enterprise AI tools. Success in this position means operating with curiosity and continual learning, both independently and within cross-functional teams. At SolarWinds, your impact will span model creation, deployment, monitoring, and support for data-driven strategies in a fast-paced, people-first environment.

Responsibilities:

• Own the entire data science project lifecycle, from identifying the opportunity, training, and deploying models right through to achieving stated business objectives.
• Do deep analysis to fully grasp the data, spotting key trends and drawing conclusions. Deliver analytical insights focused specifically on improving organizational operations.
• Apply advanced statistical modeling techniques to business outcomes. This includes regression, time series, longitudinal and categorical analysis, survival analysis, clustering, segmentation, neural networks, deep learning, embedding, vector similarity, and natural language processing.
• Effectively process data wrangling and preprocessing across all types of sources—structured, semi-structured, and unstructured—to prepare clean data for analysis and modeling.
• Implement and maintain robust ML pipelines using MLOps frameworks and effective model lifecycle management. Monitor deployed models over time using statistical evaluation and measure their business effectiveness based on model results and KPI impact.
• Act as the primary technical lead for cross-functional projects, driving alignment between business, engineering, and data science. Collaborate closely with business intelligence, development, engineering, data governance, and other relevant functional teams. Mentor junior data scientists and share best practices across the team.
• Partner with stakeholders to translate business challenges into impactful, data-driven solutions. Engage regularly to review project results and build relationships for future analytic work. Present project work in clear, actionable terms to both technical experts and non-technical decision-makers.
• Influence new analysis and machine learning projects, technical direction, and standards for model development and MLOps practices
• Answer complex data-related questions independently, requiring minimal supervision. Continually drive to find the next answer through data and business processes.
Nice to Have
• Support the development and maintenance of a Retrieval-Augmented Generation (RAG) LLM application hosted on Azure. Integrate generative AI capabilities into internal tools, products, or employee-facing applications.
• Assist with prompt engineering and model fine-tuning, tailoring solutions to specific domains and dynamic business requirements. Evaluate LLM outputs for accuracy, relevance, bias, and business alignment, iteratively refining systems to ensure quality and reliability.
• Ability to keep up with trending AI functionality and apply it to adding value to the business in bespoke solutions.
• Use, test and implement Enterprise AI solutions to influence the business through AI.

Qualifications

• 6+ years in data science, artificial intelligence, or business intelligence within a collaborative environment.
• Proficiency with Python, SQL, and ML/AI frameworks (such as TensorFlow, PyTorch, Scikit-learn, MLFlow), with an emphasis on data manipulation, API integration, and machine learning workflows.
• Strong analytical drive, results-oriented, and intellectually curious with a clear focus on business impact.
• Solid knowledge of data science methodologies (regression, time series, longitudinal and categorical analysis, survival analysis, clustering, segmentation, neural networks, deep learning, NLP).
• Experienced in applying statistical concepts and methodologies while confidently avoiding common pitfalls.
• Excellent communication skills, able to present insights clearly to both technical and non-technical stakeholders.
• Outstanding project, organizational, and time management skills; adept at prioritizing complex assignments.
• Comfortable with version control, branching strategies, and collaborative workflows using Git.
• Able to build, run, and debug Docker containers, familiarity with container orchestration basics.
• A proven problem-solver in fast-paced, dynamic settings.
• Data Curious. Eager to explore data and outcomes actively.
• Experience with Azure, OpenAI, search index, container apps, CI/CD pipelines, RAG architectures, chatbots, front end skills (HTML/CSS/JS) is a plus.
• Experience in Tableau is a plus.
• Experience in Google Big Query is a plus.
• Bachelor’s in statistics, data science, computer science, or related field; postgraduate degree is a plus.