Position Summary: MedReview is looking for a talented and experienced Data Scientist to join our dynamic team. As a part of our team, you will leverage your analytical skills and expertise in machine learning to extract insights from complex datasets and drive data-driven decision-making across our organization. You will collaborate closely with cross-functional teams to develop predictive models, uncover actionable insights, and solve challenging business problems. As part of a global team of developers and analysts, the Data Scientist will work with a larger team to design, build, validate, refine, and operationalize models. This position will sit in Austin, Texas. However, for the right fit, we may consider remote. Responsibilities: - Problem Identification: Collaborate with stakeholders to identify business challenges that can be solved through data analysis. - Data Collection & Preparation: Gather data from various sources (SQL databases, APIs, web scraping), then clean and "wrangle" it to ensure accuracy for modeling. - Model Development: Design and implement algorithms and predictive models using machine learning techniques to forecast outcomes or categorize information. - Exploratory Data Analysis (EDA): Analyze datasets to uncover hidden patterns, trends, and anomalies. - Communication & Visualization: Translate technical findings into "data stories" using tools like Tableau or Power BI to influence executive decisions. Qualifications: - Master’s degree or bachelors degree and equivalent experience in a quantitative field (Math, CS, Stats) - Programming: Proficiency in Python or R along with SQL for database querying. - Mathematics & Statistics: Strong foundation in linear algebra, calculus, and statistical modeling. - Machine Learning: Experience with frameworks like TensorFlow, PyTorch, or scikit-learn. Soft Skills: Critical thinking, curiosity, and the ability to explain complex concepts to non-technical audiences. Experience working with global and remote teams

• Own end-to-end media modeling (MMM), from data preparation and model development, to validation, calibration, and stakeholder delivery • Design, launch, and maintain incrementality testing frameworks, in partnership with paid media partners, including geo-holdout and platform-native lift study designs • Translate model outputs into actionable budget allocation and channel investment recommendations • Partner with channel leads to develop and maintain a structured learning agenda across paid media • Automate recurring measurement processes to improve accuracy, scalability, and speed-to-insight • Contribute to raising the analytical bar across the team through code review, documentation, and knowledge sharing

Requisition Number: 2357144 At UnitedHealthcare, we're simplifying the health care experience, creating healthier communities and removing barriers to quality care. The work you do here impacts the lives of millions of people for the better. Come build the health care system of tomorrow, making it more responsive, affordable and optimized. Ready to make a difference? Join us to start Caring. Connecting. Growing together A Senior Data Scientist with a general analytics focus, acting as a versatile contributor across EAR projects impacting the UHC COVE. This role functions as a primary contributor on data science efforts, complementing specialized AI roles by focusing on broad data analysis, statistical modeling, and cross-functional analytic support. It requires an advanced understanding of data and analytic techniques, plus the flexibility to prepare and shape data for analysis. You'll enjoy the flexibility to work remotely * from anywhere within the U.S. as you take on some tough challenges. For all hires in the Minneapolis or Washington, D.C. area, you will be required to work in the office a minimum of four days per week. Primary Responsibilities: - Exploratory Data Analysis (EDA): Dive deep into project datasets to uncover patterns, trends, and insights. Conduct thorough exploratory analyses that inform project direction or hypothesis generation, identifying key statistics and relationships in the data that drive decision-making - Feature Engineering & Data Preparation: Create and refine features for use in machine learning models or reporting. Work on combining data from multiple sources, cleaning and transforming raw data into structured formats (with help from data engineers when needed). Ensure that analytic datasets are comprehensive and of high quality for downstream use - Statistical Modeling & Insights: Develop statistical models or simpler machine learning models to solve a variety of project needs beyond the primary AI components. This could include predictive analytics (e.g., regression models for trend analysis), segmentation/clustering to understand different user or patient groups, or A/B test analysis for pilot interventions. Interpret model results and validate their accuracy and reliability - Business Intelligence & Reporting: Translate data findings into actionable insights for business stakeholders. Build dashboards, visualizations, or custom reports that track key metrics for the project. Present analytical results in clear business terms, and make recommendations based on data (e.g., identifying areas of improvement in program outcomes or user engagement metrics) - Collaboration & Support: Work closely with other team members to support overall project goals. Provide analytical assistance such as validating the main AI model outputs, performing deep-dive analyses when anomalies arise, or answering ad-hoc analytical questions from leadership. Also collaborate with data engineers to ensure that data pipelines align with analytical requirements (for example, suggest additional data sources or transformations that could enhance model performance) You'll be rewarded and recognized for your performance in an environment that will challenge you and give you clear direction on what it takes to succeed in your role as well as provide development for other roles you may be interested in. Required Qualifications: - Analytics Experience: 5+ years in data science, analytics, or related fields with demonstrable impact. Solid background in analyzing complex datasets and building models or analytical solutions. Experience should cover end-to-end analytics: gathering requirements, data acquisition, analysis/modelling, and delivering results - Proven Technical Skills: Proficiency in programming for data analysis (Python or R) and solid SQL capabilities for querying databases. Comfort with relevant libraries and tools (pandas, numpy, matplotlib or ggplot2, etc. for analysis; scikit-learn or similar for modeling). Solid understanding of statistical concepts (e.g., significance testing, distributions, regression analysis) and when to apply them - Data Handling Experience: Experience working with large-scale or complex data (such as claims or transactional data, etc.). Able to write efficient code or queries to manipulate and aggregate data. Familiarity with data warehousing concepts and possibly big data tools (like Hadoop/Spark) is helpful to manage volume - Proven Problem-Solving & Communication Skills: Excellent analytical thinking with the ability to break down ambiguous problems into solvable parts. Attention to detail in validating results. Solid written and verbal communication skills to explain findings to non-technical stakeholders and document methodologies clearly Preferred Qualifications: - Data Engineering Experience: Experience with or exposure to data engineering tasks - Big Data Technologies Experience: Familiarity with Apache Spark for data processing. Ability to leverage cluster computing for speeding up large data analysis - Domain Insight Experience: Background in healthcare data or operational analytics in health services. Knowledge of healthcare concepts (like patient vitals, claims, clinical metrics) - Advanced Analytics Methods Experience: Experience in specialized areas like natural language processing (NLP), time-series analysis, or geospatial analysis, depending on project needs, is an added advantage *All employees working remotely will be required to adhere to UnitedHealth Group's Telecommuter Policy Pay is based on several factors including but not limited to local labor markets, education, work experience, certifications, etc. In addition to your salary, we offer benefits such as, a comprehensive benefits package, incentive and recognition programs, equity stock purchase and 401k contribution (all benefits are subject to eligibility requirements). No matter where or when you begin a career with us, you'll find a far-reaching choice of benefits and incentives. The salary for this role will range from $91,700 to $163,700 annually based on full-time employment. We comply with all minimum wage laws as applicable. Application Deadline: This will be posted for a minimum of 2 business days or until a sufficient candidate pool has been collected. Job posting may come down early due to volume of applicants. At UnitedHealth Group, our mission is to help people live healthier lives and make the health system work better for everyone. We believe everyone-of every race, gender, sexuality, age, location and income-deserves the opportunity to live their healthiest life. Today, however, there are still far too many barriers to good health which are disproportionately experienced by people of color, historically marginalized groups and those with lower incomes. We are committed to mitigating our impact on the environment and enabling and delivering equitable care that addresses health disparities and improves health outcomes - an enterprise priority reflected in our mission. UnitedHealth Group is an Equal Employment Opportunity employer under applicable law and qualified applicants will receive consideration for employment without regard to race, national origin, religion, age, color, sex, sexual orientation, gender identity, disability, or protected veteran status, or any other characteristic protected by local, state, or federal laws, rules, or regulations. UnitedHealth Group is a drug - free workplace. Candidates are required to pass a drug test before beginning employment.

• Work cross functionally to identify opportunities for leveraging company data to drive business solutions. • Mine and analyze data from claims databases to drive optimization and improvement of product development, sales, marketing techniques and business strategies. • Assess the effectiveness and accuracy of new data sources and data gathering techniques. • Develop custom data models and algorithms to apply to data sets. • Use predictive modeling to increase and optimize customer experiences, revenue generation, ad targeting and other business outcomes. • Coordinate with different functional teams to implement models and monitor outcomes. • Develop processes and tools to monitor and analyze model performance and data accuracy including responsible AI.