Role Description The Data Science Operations Lead sits at the intersection of Data Science, Engineering, and IT Architecture: a senior individual-contributor role focused on the operational side of the model lifecycle, including deployment, monitoring, scaling, and maintenance. This role exists to keep a growing portfolio of models in production reliable, observable, and well-governed without pulling Data Scientists away from building new capabilities. - Model deployment partnership: Serve as Data Science's primary counterpart to the Architecture / Platform Engineering team on model deployment. Own the day-to-day collaboration, hand-offs, and coordination. - Production reliability and incident response: Act as first point of contact for production issues (outages, errors, degraded endpoints) across all deployed models and endpoints. This role carries an explicit on-call / off-hours availability expectation. - Resilient, error-aware systems: Bring rigor to error handling and fault tolerance. Design and enforce practices that prevent errors before they happen. - Monitoring and observability: Establish and maintain the monitoring and observability needed to manage a portfolio of production models. - Deployment expertise and team enablement: Develop a detailed understanding of the deployment system and help Data Scientists move from experiment to production quickly and safely. - Governance and quality: Own versioning, reproducibility, and operational governance for models in production. Qualifications - Hands-on experience operating ML or software systems in production: an MLOps, DevOps, SRE, platform, or data science background with demonstrated production ownership. - Strong working knowledge of CI/CD pipelines, deployment automation, and a major cloud platform (AWS, Azure, or GCP). - Demonstrated expertise in error handling, fault tolerance, and designing systems that fail gracefully. - Proficiency in Python (R a plus), and a working understanding of how ML models are packaged, served, monitored, and retrained. - Comfort serving as first point of contact for production issues, including an on-call / off-hours expectation. - A teaching disposition, with the ability to translate complex infrastructure into clear guidance for colleagues. Requirements - Experience standing up monitoring and observability for a portfolio of production models or services. - Familiarity with containerization (Docker) and orchestration (Kubernetes), infrastructure-as-code, and model-serving frameworks. - Familiarity with MLOps tooling such as MLflow, Airflow, or Kubeflow, or managed equivalents. - Experience working across an engineering/architecture boundary as a liaison or embedded operations partner. - Pragmatic use of AI tooling to accelerate operations and code-quality work. Benefits - For more information about our benefit offerings, please visit our careers page at https://www.echo.com/company/careers . - Compensation: $129,352.00-188,077.00 per year. - This role is eligible for a bonus that is based on a combination of personal and business performance.

Role Description The Lead Data Scientist (Experimentation) designs and operates the learning engine of the CAHPS & HOS teams, ensuring actions produce reliable learning. This role owns study design and measurement strategy across CAHPS & HOS, builds lightweight testing frameworks, and accelerates feedback loops to ensure every action becomes an opportunity to generate insight, reduce uncertainty, and improve future decisions. The Lead Data Scientist (Experimentation) is responsible for developing and maintaining robust study designs and measurement strategies for interventions impacting CAHPS & HOS. This role partners with operations teams and external vendors to ensure sound methodology and rapid, continuous measurement for pilots and interventions. This position exercises independent judgment and decision-making on complex issues regarding study design, analytics, and learning strategies, working under minimal supervision to ensure that insights reliably drive action and improvement. Key Responsibilities - Develop and own rigorous study designs and measurement plans for CAHPS & HOS interventions, pilots, and programs. - Build, maintain, and scale lightweight A/B testing frameworks to facilitate rapid, consistent experimentation and learning. - Create and maintain a centralized inventory of experiments, insights, and learnings to support organizational knowledge and strategy. - Partner with operations teams and external vendors to ensure methodological integrity and effective, continuous measurement. - Conduct early and consistent evaluation of causal impact, ensuring actionable insights are generated and communicated to stakeholders. - Iterate on study and measurement approaches to accelerate feedback loops and reduce uncertainty in decision-making. Qualifications - Bachelor’s degree in Data Science, Statistics, Epidemiology, or related field. - 5+ years of experience in study design, experimentation, or applied data science. - Expertise in A/B testing, causal inference, and measurement strategies. - Proficiency in programming languages and statistical tools (e.g., Python, SQL). - Demonstrated ability to articulate ideas effectively in both written and oral forms with strong collaboration skills. - Ability to work independently and exercise sound judgment on complex analytic issues. Preferred Qualifications - Master’s degree or PhD in a quantitative discipline. - Experience in healthcare analytics, survey research, or Stars performance measurement. - Familiarity with automation tools for experiment workflow. - Knowledge of learning design principles and rapid feedback methodologies. Requirements - At minimum, a download speed of 25 Mbps and an upload speed of 10 Mbps is required; wireless, wired cable or DSL connection is suggested. - Satellite, cellular and microwave connection can be used only if approved by leadership. - Employees who live and work from Home in the state of California, Illinois, Montana, or South Dakota will be provided a bi-weekly payment for their internet expense. - Humana will provide Home or Hybrid Home/Office employees with telephone equipment appropriate to meet the business requirements for their position/job. - Work from a dedicated space lacking ongoing interruptions to protect member PHI / HIPAA information. - Occasional travel to Humana's offices for training or meetings may be required. Benefits - Medical, dental and vision benefits. - 401(k) retirement savings plan. - Time off (including paid time off, company and personal holidays, volunteer time off, paid parental and caregiver leave). - Short-term and long-term disability. - Life insurance and many other opportunities. Scheduled Weekly Hours 40 Pay Range The compensation range below reflects a good faith estimate of starting base pay for full time (40 hours per week) employment at the time of posting. The pay range may be higher or lower based on geographic location and individual pay will vary based on demonstrated job related skills, knowledge, experience, education, certifications, etc. $142,300 - $195,700 per year. This job is eligible for a bonus incentive plan based upon company and/or individual performance. Application Deadline 06-22-2026 Equal Opportunity Employer It is the policy of Humana not to discriminate against any employee or applicant for employment because of race, color, religion, sex, sexual orientation, gender identity, national origin, age, marital status, genetic information, disability or protected veteran status.

• Manage day-to-day scientific operations activities, supporting your team to deliver quality outcomes • Leading the management and analysis of clinical trial data, ensuring high standards of data accuracy, integrity, and regulatory compliance • Collaborating with cross-functional teams to design and implement data strategies that support clinical research objectives and optimize data workflows • Overseeing data collection, processing, and validation to ensure consistency and reliability across clinical trials • Providing strategic guidance on data management practices, including the development of data analysis plans and reporting methodologies • Managing and mentoring data teams, fostering a collaborative environment to drive continuous improvement in data handling and analysis

Role Description - Lead and execute clinical and operation advanced analytics or system improvements. - Develop novel data science methodologies to generate deep insights from disparate data sources. - Provide subject matter expertise to teams in identification and application of the right data science methodology for a business problem. - Formulate the right business problem in collaboration with business and create necessary analysis plans. - Develop machine learning methodologies both supervised and unsupervised as appropriate to the business problem. - Develop scalable and robust code in relevant languages such as R or Python in support of data science projects. - Perform exploratory data analysis, system design analysis, integration design analysis, and database design analysis to generate insights and help answer business questions. - Ensure the life cycle management of the models is maintained through code repositories and version control tools. - Perform data engineering, data preprocessing, and data wrangling activities utilizing different approaches such as SAS, SQL, NOSQL, and graph models. - Perform thorough validation of the machine learning output to ensure a high-quality work product. Qualifications - Minimum of 5 years’ experience with a relevant master’s degree in data science, statistics, informatics, operations research, computer science, engineering, or information systems. - Experience in data science and visualization, including building data ecosystems and pipelines, data processing, data wrangling, and analytics (reports, BI dashboards, ML models) with SQL and Python or R programming knowledge. - Proven ability to translate and influence business requirements against existing analytics and data integration capabilities while identifying challenges and necessary solutions. - A proven record in shaping and delivering key programs that optimized performance and delivered project support in matrix environments. - Experience in advanced analytics processes, including gaining insight from structured and unstructured data, predictive modeling, and optimization. - Strong working knowledge of traditional statistical methodologies. - Strong working knowledge of machine learning methodologies, both supervised and unsupervised learning methods. - High proficiency in either Python or R. - Familiarity with best practices in machine learning model development, validation, and deployment. - Solid understanding of life cycle management of code and code repositories. - Extensive experience with data wrangling, data preprocessing, and data engineering techniques on large datasets. - Technical expertise, preferably in pharmaceutical clinical or a regulated industry operation, with extensive experience in diverse and complex data integration, warehousing, and ingestion. Requirements - This role is not eligible for UK visa sponsorship. Company Description IQVIA is a leading global provider of clinical research services, commercial insights, and healthcare intelligence to the life sciences and healthcare industries. We create intelligent connections to accelerate the development and commercialization of innovative medical treatments to help improve patient outcomes and population health worldwide. Learn more at https://jobs.iqvia.com . IQVIA is committed to integrity in our hiring process and maintains a zero tolerance policy for candidate fraud. All information and credentials submitted in your application must be truthful and complete. Any false statements, misrepresentations, or material omissions during the recruitment process will result in immediate disqualification of your application, or termination of employment if discovered later, in accordance with applicable law. We appreciate your honesty and professionalism.