• Responsible for designing evaluation frameworks, testing methodologies, and telemetry analysis for agentic systems, with working knowledge of LangChain and LangGraph to support and enhance engineering efforts. • Build, validate, and optimize data models focused on generative AI and large language models. • Create and refine methods to measure AI model and agent performance, quality, and impact. • Collaborate with product and engineering teams to inform and support integration of AI solutions (e.g., Gemini, OpenAI, Azure AI). • Stay current on advances in data science, machine learning, and generative AI. • Mentor junior data scientists and engineers. • Communicate clearly with stakeholders, presenting insights and recommendations. • Apply statistical, econometric, and machine learning methods to generate actionable insights and support decision-making. • Identify, integrate, and leverage key data sources to enhance analytics. • Develop scalable data structures and analytic products. • Lead end-to-end analytics and modeling projects, including data gathering, feature engineering, model development, evaluation, and monitoring. • Provide insights to encourage data-driven decision-making throughout the organization. • Mentor teams on data science best practices and contribute thought leadership through blogs or articles. • Develop and deliver training on analytics tools and data science techniques (e.g., Alteryx, Tableau).

Join the transformative team at City of Hope, where we're changing lives and making a real difference in the fight against cancer, diabetes, and other life-threatening illnesses. City of Hope’s growing national system includes its Los Angeles campus, a network of clinical care locations across Southern California, a new cancer center in Orange County, California, and treatment facilities in Atlanta, Chicago and Phoenix. Our dedicated and compassionate employees are driven by a common mission: To deliver the cures of tomorrow to the people who need them today. ** This is a Fully Remote Opportunity within the United States** The Data Scientist in this role will query and analyze large complex datasets to discover insights to apply to an evolving cancer care delivery environment. The Data Scientist will use data mining, statistics and machine learning to understand relationships and patterns in data, build predictive models and propose actionable solutions. As a successful candidate, you will: - Collaborate with administrative leaders, clinicians and IT specialists - Query large datasets from multiple systems - Build machine learning models - Design and implementation of pilots for validation and optimization of models in real time - Produce technical documentation, visualizations and presentations - Project management for complex tasks - Completes own work independently - Performs other related duties as assigned or requested. Your qualifications should include: - Master’s degree with 0+ years of experience; or Bachelor’s degree with 3+ years of experience Preferred: - Familiarity with a health care environment and EHR data - Familiarity with time series, deep learning, and Natural Language Processing (NLP) - Proficient designing, training, testing, deploying, and monitoring machine learning models to solve a wide array of problems - Familiarity with cloud services like Microsoft Azure and version control systems (Git) - Experience with Generative AI and Large Language Models (LLMs), RAG, fine-tuning, embeddings, prompt engineering, and agentic workflows - Awareness of the most recent trends and developments in the Data Science / Machine Learning fields - Experience with Snowflake a plus - Proficient developing machine learning models for classification, regression and clustering - Experience with relational databases and SQL queries. - Proficient in Python and its machine learning libraries like pandas, scikit-learn, PyTorch, LangChain, etc - Familiar embedding LLMs into workflows with packages like LangChain and LangGraph - Advanced knowledge of probability, statistics, and scientific computing - Ability to work independently and use creative approaches to problem- solving Capable of turning questions into testable hypotheses, extracting data from complex databases, creating and evaluating statistical models Experience in data visualization and ability to communicate results to a non-technical audience Experience in completing end-to-end projects with minimal to no supervision Experience in being a part of big projects with many internal and external teams involved Experience managing collaborative data science projects City of Hope employees pay is based on the following criteria: work experience, qualifications, and work location. City of Hope is an equal opportunity employer. To learn more about our Comprehensive Benefits, please CLICK HERE.

• Drive the adoption of strategic and tactical recommendations that are based on (1) deep and hands-on experience with Reddit products, and (2) data skills and understanding, to maximize our value to Reddit consumers. • Serve as a thought-partner for product managers, engineering managers and leadership from your respective product domain, communicating and shaping the roadmap and strategy for Reddit by identifying actionable and impactful insights through deep-dive analyses and analytics insights. • Be proactively involved in all phases of product development, including but not limited to, ideation, exploratory analysis, opportunity sizing, metrics design, offline modeling, experimentation and decision-making, post-launch monitoring/measurements etc. • You will have a keen interest in the collection and quality of underlying data (experiment design and analysis, data deep dive), along with working on ETLs, reporting dashboards and data aggregations needed for business tracking and/or ML model development.

• Experimentation & Causal Inference: Design and implement scalable A/B tests to train causal inference models and multi-armed bandits to optimize registration, trial conversion, and retention strategies. • Automation & Personalization: Build and scale automation tools for experimentation and segmentation, ensuring teams can efficiently test and iterate on hypotheses. • Growth Optimization: Develop predictive models and statistical frameworks to improve trial-to-subscription conversion, reduce churn, and enhance overall customer LTV. • User Segmentation & Personalization: Leverage clustering, behavioral analysis, and geospatial data to identify key user segments and tailor experiences accordingly. • Product Development: Collaborate with product managers and engineers to conceive and develop innovative product ideas, driving feature iteration and development to make impact. • Cross-functional Collaboration: Partner closely with growth, lifecycle marketing, and user acquisition teams to translate insights into scalable marketing and product strategies. • Data Infrastructure & Tooling: Work with data engineering to improve the data ecosystem, ensuring robust, scalable pipelines for experimentation and analysis. • Thought Leadership: Act as a mentor and thought leader within the data science team, helping to define best practices in experimentation, machine learning, data-driven decision-making, and agentic AI (AI-Native) best practices.