Job Title: Senior Applied Scientist Company Name: Empower Pharmacy Job Url: https://www.linkedin.com/jobs/view/4398926931/?eBP=NON_CHARGEABLE_CHANNEL&refId=YjbStl59dONYk2r3J3jiqw%3D%3D&trackingId=c7hzXNOUBmq6nIA%2BORSseA%3D%3D&trk=flagship3_search_srp_jobs Job Description: Empower Pharmacy Share Show more options Senior Applied Scientist  United States · 59 minutes ago · 2 people clicked apply Promoted by hirer · Responses managed off LinkedIn Remote Full-time Apply Save Save Senior Applied Scientist  at Empower Pharmacy How your profile and resume fit this job Get AI-powered advice on this job and more exclusive features with Premium. Try Premium for $0 Show match details Tailor my resume Help me stand out About the job Position Summary The Senior Applied Scientist delivers high-impact AI and machine learning solutions that accelerate business outcomes, owning end-to-end model development from problem framing through deployment and optimization. This role operates at the intersection of data, engineering, and business, scaling intelligent systems that enhance decision quality, speed, and operational precision. AI is leveraged as a force multiplier to unlock productivity, automate complexity, and enable predictive insights across the enterprise. Within Empower’s hyper-growth, highly regulated 503A/503B environment, this role ensures compliant, reliable, and scalable AI innovation. The position demonstrates exceptional strategic thinking, disciplined execution, and continuous learning agility while influencing cross-functional stakeholders and advancing enterprise AI maturity through robust, production-grade solutions. Duties and Responsibilities AI Solution Development Model Development: Design, build, and deploy scalable machine learning and AI models, including NLP and LLM-based systems, translating complex business problems into production-ready solutions that improve decision-making speed, operational efficiency, and measurable business outcomes while ensuring robustness, performance, and adaptability in evolving enterprise environments. System Architecture: Architect end-to-end AI systems, including data pipelines, model training frameworks, and deployment environments, ensuring scalability, reliability, and maintainability while integrating modern approaches such as retrieval-augmented generation to enhance contextual intelligence and enterprise-wide usability of AI-driven applications. Performance Optimization: Continuously refine models through experimentation, evaluation, and monitoring, leveraging AI-driven insights to improve accuracy, reduce latency, and enhance system performance, while proactively addressing drift, bias, and evolving data patterns to sustain long-term model effectiveness and business value. AI Engineering And MLOps Pipeline Engineering: Build and maintain robust machine learning pipelines with automated testing, CI/CD integration, and reproducibility, enabling rapid experimentation and deployment while ensuring consistency, traceability, and scalability across the model lifecycle in alignment with enterprise engineering standards. Model Governance: Implement rigorous evaluation frameworks, guardrails, and governance practices to ensure responsible AI usage, focusing on explainability, compliance, and risk mitigation within regulated environments, while maintaining transparency and accountability across all deployed AI systems. Monitoring Systems: Develop and manage monitoring solutions that track model performance, detect drift, and trigger retraining workflows, ensuring sustained accuracy and reliability while enabling proactive intervention and continuous improvement of AI systems operating in production environments. Cross-Functional Impact Stakeholder Alignment: Collaborate with engineering, data, and business leaders to identify high-value AI opportunities, clearly communicate technical concepts, and influence decision-making, ensuring alignment between AI initiatives and strategic business priorities while maximizing return on investment. Problem Translation: Translate complex business challenges into actionable AI solutions, balancing technical feasibility with business impact, and leveraging advanced analytics and machine learning to drive innovation, efficiency, and competitive advantage across multiple functional domains. Technical Leadership: Mentor peers and elevate technical excellence by sharing best practices, promoting AI literacy, and contributing to enterprise AI strategy, fostering a culture of innovation, accountability, and continuous improvement across teams and initiatives. Knowledge and Skills Deep expertise in machine learning, NLP, LLMs, and RAG architectures, with strong proficiency in Python, SQL, and modern AI frameworks, enabling development of scalable, production-grade intelligent systems. Strong understanding of MLOps practices including CI/CD, model monitoring, reproducibility, and lifecycle management within cloud environments such as AWS or Azure. Ability to design AI systems that meet regulatory, compliance, and governance requirements while maintaining high performance, explainability, and reliability in complex enterprise settings. Advanced problem-solving, communication, and collaboration skills, with the ability to translate complex technical concepts into actionable business insights and influence cross-functional stakeholders. Key Competencies Customer Focus: Builds trust through customer-centric solutions Strategic AI: Guides responsible AI adoption and adaptation Optimizes Work Processes: Drives efficiency with continuous improvement Collaborates: Partners effectively to achieve shared goals Resourcefulness: Secures and deploys resources efficiently Manages Complexity: Simplifies and solves complex challenges Ensures Accountability: Delivers on commitments with integrity Situational Adaptability: Adjusts approach to shifting conditions Communicates Effectively: Tailors messages to diverse audiences Values People: Empowering people defines who we are Quality: Excellence in every product, every time Service: Serving others is our highest purpose Innovation: Advancing care through technology and discovery Experience And Qualifications Minimum of 5 years of experience in applied machine learning, artificial intelligence, NLP, data science, or a related quantitative or engineering discipline. Bachelor’s degree in Computer Science, Data Science, Statistics, Applied Mathematics, Engineering, or a related field required; Master’s degree preferred. Strong problem-solving, collaboration, and communication skills with a high degree of curiosity, adaptability, accountability, and the ability to translate complex AI/ML concepts into practical business solutions. Proficiency in Python and SQL, machine learning model development, NLP, LLMs, prompt engineering, embeddings, vector search, RAG system design, MLOps practices (CI/CD, monitoring, reproducibility), and cloud platforms such as AWS or Azure; experience with frameworks and tools such as scikit-learn, PyTorch, TensorFlow, Hugging Face, LangChain, and OpenAI APIs strongly preferred. Preferred: AWS Certified Machine Learning – Specialty, Microsoft Azure AI Engineer Associate, or equivalent certifications. Benefits Employee Benefits, Health and Wellness: We offer comprehensive benefits to support your health, well-being, and future, including medical, dental, and vision coverage, paid time off, 401(k) matching, wellness perks, IV therapy, and compounded medications. Learn more: https://careers.empowerpharmacy.com/benefits/ Physical Requirements While performing the responsibilities of the job, the employee is required to talk and hear. The employee is often required to remain in a stationary position for a significant amount of the workday and frequently use their hands and fingers to handle or feel in order to access, input, and retrieve information from the computer and other office productivity devices. Employees are regularly required to move about the office and around the corporate campus. The employee is regularly required to stand, walk, reach with arms and hands, climb or balance, and to stoop, kneel, crouch or crawl. See more