Head of Data

📋 Description

• • As the Head of Data at AllCare Home Health Services, Inc., you will be instrumental in establishing and scaling our data science capabilities from the ground up. This is a pivotal leadership role focused on defining the long-term strategic vision for data science and driving its hands-on execution across critical areas such as machine learning, personalization, experimentation, forecasting, fraud prevention, and internal decision intelligence. You will operate at the dynamic intersection of product development, engineering, and analytics, shaping the future of our data-driven initiatives.
• • Your primary responsibility will be to develop and deploy sophisticated models that directly influence key business metrics, including user engagement, monetization strategies, and robust risk management across our global consumer products. Initially, you will oversee and manage an external data science ML agency, ensuring continuity and expertise while simultaneously building and nurturing AllCare's internal data science and AI team. This dual approach ensures immediate impact while fostering sustainable internal growth.
• • A significant aspect of your role will be to own and champion AllCare's applied AI strategy. This includes exploring and integrating cutting-edge technologies like Large Language Models (LLMs) to power new product features and enhance internal operational efficiency through AI-driven tools. You will be the architect of our AI future, guiding its development and application across the organization.
• • **Strategy and Leadership:**
• • Define, articulate, and own the comprehensive data science roadmap for AllCare, aligning it with overarching business objectives and growth strategies.
• • Build, mentor, and lead a high-performing internal data science and AI team, establishing best practices, standards, and a culture of innovation and continuous learning.
• • Effectively manage and coordinate the activities of an external ML agency, ensuring seamless collaboration and knowledge transfer until the internal team is fully established and capable.
• • Foster strong, collaborative relationships with cross-functional teams, including engineering, product management, design, marketing, operations, fraud prevention, and senior leadership, to champion data-informed decision-making across the organization.
• • Translate complex technical data science concepts and findings into clear, actionable recommendations and compelling narratives for business stakeholders at all levels.
• • Lead the strategic development and implementation of AllCare's applied AI initiatives, with a particular focus on leveraging LLMs for both customer-facing features and internal productivity tools.
• • **Machine Learning and Personalization:**
• • Design, build, and maintain advanced machine learning models for critical applications such as user personalization, recommendation engines, engagement prediction, churn analysis, and Lifetime Value (LTV) forecasting.
• • Oversee the deployment and rigorous evaluation of ML models across both online and offline data pipelines, implementing continuous retraining and monitoring processes to ensure optimal performance and accuracy.
• • Partner closely with the engineering teams to seamlessly integrate ML models into AllCare's backend systems and mobile web products, prioritizing performance, scalability, and maintainability.
• • Design and integrate innovative LLM-based capabilities, including but not limited to semantic search functionalities, natural language assistance for users and internal teams, automated content generation, and model-augmented decision-making engines.
• • Evaluate, fine-tune, and potentially distill large foundation models (such as OpenAI, Claude, Llama, Gemini, etc.) to best suit AllCare's specific product requirements and internal workflows, ensuring efficiency and effectiveness.
• • **Fraud Detection and Risk Prevention:**
• • Develop and implement sophisticated fraud detection systems leveraging advanced techniques like anomaly detection and behavioral modeling to safeguard our platform and users.
• • Build robust scoring systems to identify and mitigate various forms of fraudulent activity, including event manipulation, multi-accounting, and other suspicious behaviors.
• • Collaborate with product and operations teams to design and refine effective block rules, risk thresholds, Time-to-Compromise (TTC) validation systems, and automated risk classification mechanisms.
• • Proactively monitor emerging fraud trends and patterns, identifying and addressing potential risks before they impact the business.
• • **Data Infrastructure and Model Operations (MLOps):**
• • Define technical requirements and collaborate closely with engineering to build and maintain scalable, reliable data pipelines essential for model training, inference, experimentation, and monitoring.
• • Establish and implement robust MLOps practices, including CI/CD pipelines for ML models, automated retraining schedules, comprehensive monitoring frameworks, and effective version control.
• • Ensure the highest standards of data quality, comprehensive documentation, and metadata management across all data science initiatives and assets.
• • Build and manage the necessary infrastructure for LLM workloads, including vector databases, embedding pipelines, prompt evaluation frameworks, and safety/compliance checks.
• • **Compliance, Privacy, and Responsible AI:**
• • Ensure all data science initiatives strictly adhere to global privacy regulations, including GDPR, Japan APPI, Korea PIPA, and CCPA.
• • Work in tandem with the Data Protection Officer (DPO) and legal teams to design and implement privacy-by-design principles in all data science solutions.
• • Establish clear guardrails and ethical guidelines for the responsible, safe, and transparent use of AI and LLM technologies within the organization.