• Serve as the primary subject matter expert and super user for the UKG platform and related HR systems • Support HRIS implementations, upgrades, optimizations, and integrations in partnership with IT/IS teams • Analyze workforce and HR data to identify trends, improve reporting, and support strategic decision-making • Create dashboards, reports, and data visualizations for HR and operational leadership • Educate and support leaders on HRIS functionality, reporting tools, and data interpretation • Troubleshoot system issues, data discrepancies, and process inefficiencies • Assist with system configuration, testing, and process documentation • Ensure data integrity, accuracy, and compliance across HR systems • Recommend process improvements and system enhancements to improve operational efficiency

• Collect, clean, interpret, and analyze data for new and ongoing safety and occupational health (SOH) projects and studies. • Document business and system requirements in collaboration with stakeholders. • Design and test complex data analytics systems. • Create data visualizations that clearly communicate and drive data-driven processes. • Conduct analytical report writing for presentations and policy memoranda. • Develop statistical research designs that create a synthesized strategy for both data collection and analysis for a DoW leadership audience. • Work closely with developers to design and implement system enhancements. • Test system and data changes to ensure accuracy and quality. • Conduct data analysis using Excel and SQL to identify trends, troubleshoot issues, and support decision-making. • Translate data findings into actionable business insights and develop briefings for leadership. • Lead and manage projects as assigned, ensuring timely delivery and stakeholder satisfaction. • Train users and develop system documentation and user guides. • Ensure adherence to data governance, quality, and security standards. • Communicate effectively with both technical and non-technical audiences. • Support the continuous improvement of safety and occupational health data systems. • Prepares senior-level responses to congressional inquiries, media requests, DoW Inspector General reviews, and Government Accountability Office findings related to SOH and Occupational and Environmental Health (OEH).

Role Description We're looking for a Head of Data Analytics to own and evolve the entire data function at BetterSleep — platform, people, and strategy. You'll manage a team of data scientists, own the roadmap for our data infrastructure, and serve as the strategic data partner to product, growth, and executive leadership. This is a player-coach role: you'll spend real time in the work — reviewing dbt models, designing data products, unblocking your team — while also setting direction and partnering with the business on the decisions that matter. The data team at BetterSleep was historically weighted toward research and data science. We've been rebuilding it with a platform-first mindset: stable pipelines, reliable definitions, self-service tooling, and governance that makes the whole organization faster. You'll inherit that momentum and lead the next phase of that build. What You’ll Do - Data Platform & Engineering - Own the architecture and reliability of the UA data pipeline — BigQuery, dbt, Jenkins, AppsFlyer/SKAN attribution, and our downstream dashboards and Mixpanel warehouse sync - Drive the migration of remaining Python transformation scripts into dbt; establish CI/CD, testing standards, and dev/prod environment hygiene - Partner with engineering on the ingestion layer (ad platform APIs, Firebase, custom subscription backend webhooks) and upstream data quality - Evaluate and introduce new tooling when it's clearly the right call; default to keeping the stack simple - Analytics & Measurement - Own the definitions, logic, and reliability of our core metrics: installs, trials, trial-to-paid, RPP, ROAS, CAC, LTV, churn - Lead our attribution methodology — MMP (AppsFlyer), SKAN 4, SSOT deduplication — and translate it clearly to the UA and executive teams - Support our experimentation program: help PMs and the UA team design tests, validate results, and build statistical muscle across the org - Experience building or managing experimentation infrastructure (A/B testing platforms, statistical significance frameworks) - Build self-service data products that reduce the number of ad hoc requests hitting the team — including AI-powered tooling (e.g. natural language querying over Mixpanel/BigQuery, automated anomaly detection, LLM-assisted reporting) where it delivers real leverage - Product Analytics - Own product analytics instrumentation strategy in partnership with engineering — event taxonomy, Mixpanel governance, Firebase event schema - Translate product analytics into actionable insight for PMs: retention curves, funnel analysis, feature adoption, onboarding optimization - Ensure the product team can answer their own questions without always needing the data team in the loop - People & Partnerships - Manage and develop two data scientists; make hiring decisions around expansion of the team as needed - Partner closely with the UA Manager, Growth PM, and Head of Product as the data team's primary business-facing contact - Set sprint cadence, manage the data backlog, and keep the team focused on high-leverage work - Data Governance & AI-Forward Operations - Drive data governance and data semantic layer development; make our visualization platform more usable for non-technical stakeholders - Help the organization identify and automate high-friction internal workflows using AI — this is a company-wide priority with executive sponsorship - Champion a culture of practical AI adoption on the data team and beyond Qualifications - 7+ years in data/analytics roles, with at least 2 years managing a data team - Hands-on analytics engineering experience: you know dbt well, you're comfortable reviewing SQL models and data pipelines, and you can unblock your engineers when things break - Deep understanding of subscription and mobile app metrics: trial conversion, trial-to-paid modeling, LTV, renewal rates, cohort analysis - Experience with mobile UA attribution — MMP (AppsFlyer, Adjust, or similar), SKAN, multi-touch — and the real-world messiness of cross-platform reporting - Comfort with BigQuery (or another cloud data warehouse) as the analytical backbone - Strong instincts for data modeling: you think in facts and dimensions, you care about grain, and you can explain why a join is producing duplicate rows - Product analytics experience: you've owned event instrumentation, built Mixpanel or Amplitude governance frameworks, and partnered with product teams to translate raw events into insight - Business partnering skills: you can translate technical constraints into plain language for product and marketing stakeholders, and you push back when the question is the wrong one - Product mindset: you think about data as a product that serves internal users, not just a function that answers requests - Practical experience building with AI/LLM tools — not just using Copilot or ChatGPT for productivity, but actually designing and shipping AI-assisted workflows, internal analytics agents, or data apps that reduce manual work. You know what's possible with today's models and you're already finding ways to apply them Bonus Points - Background in consumer subscription apps (health, wellness, entertainment, productivity) - Built internal data apps using AI frameworks (Claude/OpenAI APIs, LangChain, agent tooling) — dashboards, Slack bots, automated anomaly alerts, natural language analytics interfaces, or similar - Deep experience with attribution frameworks and data modeling The Stack - BigQuery - dbt Core - Python - AppsFlyer - Mixpanel - Firebase - Jenkins - Google Cloud Storage - Jira Benefits - Competitive salary & compensation - Excellent health, dental, and vision coverage - Retirement plan with employer matching - Commuter & lunch benefits (UberEats) - Free access to telehealth & BetterHelp services - Any hardware/software you need to succeed - A product loved by millions — and admired by the press - Awesome people to work with - The chance to help people live a better life, every day

• Label and annotate cybersecurity-related datasets to prepare them for analysis and machine learning tasks • Ensure labeling accuracy and consistency across different datasets, including threat intelligence data, incident reports, network logs, etc. • Gather data from various cybersecurity sources, including threat intelligence feeds, logs, and internal reports • Clean and preprocess data to make it suitable for analysis and modeling • Perform exploratory data analysis to uncover patterns, trends, and insights related to cybersecurity threats and vulnerabilities • Utilize statistical methods and tools to interpret data and identify potential security issues • Create and maintain dashboards and reports to communicate findings to cybersecurity stakeholders • Develop visualizations to present data in a clear and concise manner, highlighting key security metrics and trends • Work closely with analysts, data scientists, engineers, and other team members to support their data needs • Support the implementation and optimization of MLOps pipelines, leveraging data insights to deploy, monitor and scale machine learning models for different solutions • Participate in team meetings and contribute to project planning and discussions, providing data-driven insights • Document processes, methodologies, and insights gained from data analysis and labeling activities • Maintain clear records of data sources, cleaning steps, and labeling criteria to ensure reproducibility and auditability