Director, AI & Data Science

Reflex Media, Inc.•, (Multiple States)

12d•$175,000 - $225,000•Remote

About The Position

Seeking.com (Reflex Media) is hiring a Director, AI & Data Science to lead its combined Data Engineering & Analytics, AI, and Data Science team of six to seven individual contributors. The role reports directly to the Co-CEOs and is remote anywhere in the USA. It is a player-coach position, with approximately half the time spent leading and half spent coding, building models, and debugging production systems. The first priorities are to automate routine user-uploaded content moderation completely, demonstrating accuracy superior to human moderators at lower total cost, and then to build AI customer support that resolves member tickets end to end. The director owns these outcomes, including policy, data, evaluation, and production results. Future work may extend AI to fraud prevention, matching, onboarding, marketing, monetization, retention, and product development in partnership with the leaders of those functions. The role calls for a hands-on leader who manages individual contributors while continuing to ship code. The director will establish clear moderation policy and ownership, inventory and monitor production AI systems, measure and systematically eliminate human-review paths, and expand automation through controlled releases with tested rollbacks. They will build verified feedback and evaluation cycles, version datasets and other system components, and ensure production systems have monitoring, recovery plans, and failure drills. The director will report business outcomes through an executive dashboard and weekly Co-CEO reviews, including quality, cost, revenue, efficiency, and member experience. They will own AI support agents that resolve tickets and perform approved actions, and partner with other departments on further AI opportunities. They will also mentor and develop the team, recruit talent, address performance gaps, establish quarterly OKRs and KPIs, and manage work using Jira, Confluence, and Monday.

Requirements

Bachelor’s degree in Data Science, Computer Science, Software Engineering, or a closely related technical field.
Seven or more years delivering production data or machine-learning systems.
Three or more years managing teams of individual contributors.
Currently manage a hands-on team or have done so recently.
Strong Python and SQL skills, with production code personally written within the past year; candidates will code during the interview.
Experience designing, building, and operating ML and LLM systems end to end, from data pipelines through evaluation, deployment, and monitoring, on a major cloud platform.
Production architecture judgment.
Experience leading an AI automation program in moderation, trust and safety, support, or a comparable decision system, from a measured human baseline to automated decisions at scale.
Ability to measure false negatives and false positives separately, report uncertainty, keep test data clean, and build systems that improve through feedback and controlled updates.
Ability to identify and quantify cost, revenue, efficiency, or member outcomes produced by prior work.
Experience finding AI opportunities in other departments, gaining their leaders' support, and taking solutions from concept into production.
A record of mentoring engineers and scientists into more senior contributors.
Daily use of Claude, Claude Code, Cowork, or comparable AI tools.
Ability to work with regular working-hour overlap with the Singapore development team and partners.
Ability to travel for team weeks and quarterly planning in Las Vegas.

Nice To Haves

Master’s or Ph.D. in Computer Science, AI, ML, Statistics, or a related quantitative field.
Production work in multimodal or computer vision moderation, fraud detection, recommender systems, or marketing and monetization models such as propensity, pricing, and lifetime value.
Experience with AWS services including S3, Redshift, SageMaker, and Bedrock, as well as Looker and Kubernetes.

Responsibilities

Set a single written moderation policy with Product, Support, and Operations, and facilitate resolution when teams disagree.
Inventory production AI systems, identifying owners, backups, dependencies, and monitoring gaps.
Trace user uploads through model decisions, enforcement, and human review; quantify remaining human-review paths, their causes, costs, and remedies.
Evaluate AI and human moderators on the same held-out cases using reference decisions from independent, policy-qualified reviewers; report missed violations and wrongful rejections separately, with sample sizes and confidence intervals.
Close moderation automation gaps involving missing coverage, uncertain outputs, system failures, unenforced decisions, and duplicate human review.
Expand automation in controlled stages, each with a tested rollback, until routine moderation is fully automated.
Run monthly model learning cycles and release new models only when they outperform production on the same independent test.
Version datasets, policies, prompts, and models to verify gains and enable rollback.
Provide monitoring, named-owner alerts, documented recovery procedures, and regular failure drills for every production AI system.
Report quality, cost per outcome, revenue, efficiency, and member experience; reconcile savings with Finance, maintain an executive dashboard, and lead weekly reviews with the Co-CEOs.
Own AI customer support end to end, including agents that resolve tickets and execute approved actions such as subscription changes and eligible refunds.
Partner with Marketing, Product, and other departments on AI opportunities including fraud, matching, onboarding, monetization, and retention; present the Co-CEOs with a ranked opportunity list.
Own models, infrastructure, evaluation, and learning systems in cross-functional partnerships, while department leaders own their business outcomes.
Review team members' work, pair on difficult problems, provide weekly direct feedback, develop growth plans, recruit talent, and address performance gaps early.
Set quarterly OKRs for the function and each direct report, including leading and lagging KPIs for every Key Result; run work in Jira, Confluence, and Monday.
Continue hands-on technical work, including writing code, building models, and debugging production systems.