Director, Data Science
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.
What you’ll be doing...
Technical Leadership & Delivery: Architect complex data science, machine learning, and agentic AI systems end to end, from foundational capabilities through production deployment. Personally build, validate, and deploy high-complexity predictive models and machine learning solutions that solve core business and customer problems. Define enterprise-level best practices for AI/ML systems, experimentation, governance, model lifecycle management, observability, and responsible AI. Bring technical leadership and decision ownership to ambiguous problems, helping the team clarify what to build, why it matters, and how success will be measured. Establish practical guardrails for AI capabilities so user expectations are aligned with what can be delivered consistently, reliably, and with excellence.
Cross-Team Influence & Architecture: Partner with Product, Engineering, UX, and senior leadership to ensure Data Science work is integrated into the broader product strategy and roadmap. Clarify the Data Science charter across research, modeling, productization, and execution, ensuring the function is focused on the highest-value priorities. Translate business needs, customer problems, and product goals into a coherent Data Science agenda. Architect shared AI capabilities, frameworks, and standards that other teams can confidently build against and consume. Improve collaboration, documentation, handoffs, and end-to-end testing across Data Science, Product, and Engineering. Ensure AI and ML solutions are designed with the end user in mind, including how users will experience, trust, and act on the outcomes.
Model Deployment & Monitoring: Partner with software engineering and DevOps teams to deploy models and AI capabilities into production environments. Monitor model performance over time, recalibrating, optimizing, and improving systems as needed. Design and implement A/B testing and experimentation frameworks to evaluate model effectiveness, user impact, and business value. Strengthen production readiness practices, including testing, documentation, monitoring, explainability, and performance measurement.
Team Leadership & Growth: Lead, manage, and develop a small team of data scientists, owning their growth, performance, prioritization, and career development. Provide close technical guidance and management proximity to the work so the team has clear direction, coaching, and decision support. Reduce unnecessary coordination burden on individual contributors by creating clearer ownership, decision rights, and operating rhythms. Set technical and cultural direction across the team while influencing broader organizational capability. Mentor senior technical talent, raising the bar on rigor, collaboration, communication, and delivery. Grow the team through hiring, defining roles, raising the talent bar, and scaling the function as demand increases.
Qualities you possess...
10 to 15 years of experience in data science, machine learning, AI systems, or a related field, including demonstrated technical leadership across multiple teams or initiatives.
3 to 5+ years leading and growing a data science, machine learning, or AI team, with a track record of mentoring and developing senior technical talent.
Recognized depth in machine learning, AI systems, advanced analytics, or applied data science, with the ability to serve as a technical authority for the organization.
Proven track record architecting and deploying machine learning models, AI capabilities, and data science solutions into production at scale.
Experience working cross-functionally with Product, Engineering, UX, and senior leadership to align technical work with product outcomes and business strategy.
Strong experience with statistical analysis, predictive modeling, experimentation, A/B testing, and model evaluation.
Experience defining AI/ML guardrails, operating standards, documentation practices, and production readiness expectations.
Proficiency in Python and/or R and machine learning libraries such as scikit-learn, TensorFlow, PyTorch, or similar tools.
Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related field.
Excellent communication skills, with the ability to translate complex technical work into strategy, recommendations, tradeoffs, and business impact for senior stakeholders.
As a plus, you have...
Hands-on experience building agentic AI systems, LLM applications, deep learning, or NLP solutions.
Experience with AIOps, observability, infrastructure monitoring, IT operations, or enterprise software platforms.
Experience with cloud platforms such as AWS, GCP, or Azure for data science and machine learning workloads.
Familiarity with big data technologies like ClickHouse, Spark, Hadoop, Databricks, or similar platforms.
Strong knowledge of SQL and experience with database querying.
Familiarity with data visualization tools such as Tableau, Power BI, Matplotlib, or similar tools.