# ScienceLogic — Director, Data Science

- Generated: 2026-08-25 06:38:37 PM EDT
- Original/canonical posting: https://jobright.ai/jobs/info/6a408ae3b526a24fc3130f21
- Jobright source: https://jobright.ai/jobs/info/6a408ae3b526a24fc3130f21
- Posted: 2026-08-17 11:10:32 AM EDT
- Valid through: 2026-09-17 11:10:32 AM EDT
- Applicants: Not disclosed
- Work model/location: Remote, United States
- Employment type: FULL_TIME
- Compensation: Not disclosed; Director title qualifies under Keith's current criteria
- Travel: Not disclosed
- Positioning track: Technical manager
- Fit outcome: PASS — 89%

## Direct-match strengths

Hands-on ML/AI architecture, predictive modeling, forecasting, agentic AI, governance, AWS, Spark, experimentation, production readiness, and small-team leadership are excellent matches.

## Hard or material gaps

Formal A/B-testing-framework ownership is less explicit than Keith's adjacent experimentation, model-evaluation, forecasting, and product-validation evidence. Direct AIOps platform experience is adjacent through contact-center analytics, AWS, observability, and enterprise platforms.

## Evidence map

1. End-to-end ML and agentic architecture — 3/3: Direct.
2. Predictive models and forecasting — 3/3: Direct.
3. AI lifecycle, governance, observability — 3/3: Direct.
4. Product and engineering alignment — 3/3: Direct.
5. Hands-on small-team leadership — 3/3: Direct teams of 7 and 11.
6. A/B testing — 1/3: Adjacent experimentation rather than formal ownership.

## Full normalized job description

Note: The job is a remote job and is open to candidates in USA. ScienceLogic is redefining IT operations for the modern enterprise, and they are seeking a hands-on Director of Data Science to provide technical leadership and advance their AI strategy. This role involves architecting data science solutions, leading a high-performing team, and ensuring alignment with product strategy and customer needs.
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
Skills
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
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
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!
Company Overview
ScienceLogic is a provider of AI-driven monitoring solutions for hybrid cloud management. It was founded in 2003, and is headquartered in Reston, Virginia, USA, with a workforce of 501-1000 employees. Its website is http://www.sciencelogic.com.
Company H1B Sponsorship
ScienceLogic has a track record of offering H1B sponsorships, with 1 in 2026, 2 in 2025, 4 in 2024, 2 in 2023, 10 in 2022, 6 in 2021, 3 in 2020. Please note that this does not guarantee sponsorship for this specific role.

## Artifact metadata

- Resume: https://bit.ly/4g7djfs
- Cover letter: https://bit.ly/4xX1nmG
- Validation: PASS — 2-page resume (953 words), 1-page cover letter (241 words); PDF geometry, annotations, bounds, and links verified.
- Google Drive used: No