Director, Data Science

ScienceLogic

3d•Hybrid

About The Position

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. We are seeking a hands-on Director of Data Science to provide the technical leadership, organizational alignment, and product-oriented execution needed to advance our AI strategy. This leader will help mature the Data Science function, clarify ownership, and ensure AI and machine learning investments translate into reliable, meaningful product outcomes. You will spend roughly 60% of your time hands-on: architecting solutions, shaping agentic AI and ML platforms, defining technical standards, and solving complex problems. You will spend roughly 40% of your time leading a small, high-performing team: coaching, prioritizing, hiring, performance management, and building a more sustainable operating model. This role will partner closely with Product, Engineering, UX, and senior leadership to align Data Science priorities with business strategy, customer needs, and user experience. If you are energized by deep technical work, team leadership, and creating stronger alignment across functions, this role is for you.

Requirements

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.

Nice To Haves

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.

Responsibilities

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.
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.
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.
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.

Benefits

Comprehensive medical, dental and vision plans.
401(k) plan with employer match.
Flexible Paid Time Off (FTO) so that you can take the time that you need to re-energize.
Volunteer Time Off (VTO) - take two days off per calendar year to volunteer with your preferred charitable organization.
5-year Service Milestone Sabbatical.
Paid parental leave.
Generous employee referral bonus program.
Pet insurance.
HQ Office centrally located in Reston Town Center featuring a well-stocked kitchen with rotating snacks and beverages, and catered lunch on Thursdays.
Regular virtual company-wide events, including cooking classes, yoga, meditation and more.
The opportunity to learn and develop from some of the best and brightest minds in the industry!