Director, Data Science
About the role
The Director of Data Science at ScienceLogic will lead the technical and organizational aspects of our AI strategy. This role involves hands-on technical leadership, team management, and cross-functional collaboration to deliver impactful AI solutions.
Responsibilities
Architect and deploy complex data science and machine learning systems.
Define enterprise-level best practices for AI/ML systems and governance.
Clarify the Data Science charter and prioritize work to align with business strategy and customer needs.
Partner with Product, Engineering, UX, and senior leadership to integrate Data Science into the product strategy.
Monitor and optimize model performance in production environments.
Lead and grow a small team of data scientists, focusing on their growth, performance, and career development.
Translate business needs and customer problems into a coherent Data Science agenda.
Ensure AI and ML solutions are designed with the end user in mind.
Qualifications
10 to 15 years of experience in data science, machine learning, AI systems, or a related field.
3 to 5+ years leading and growing a data science, machine learning, or AI team.
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.
Strong experience with statistical analysis, predictive modeling, experimentation, A/B testing, and model evaluation.
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, able to translate complex technical work into strategy, recommendations, tradeoffs, and business impact for senior stakeholders.
Desired Qualities
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.