• Research, develop, and ship sophisticated pricing models (e.g. Tree-based, Gradient-based, Deep Learning, ensemble methods) to improve valuation accuracy for high-value assets. • Identify new data signals — emanating from data such as auction results to social sentiment — and validate their predictive power through rigorous backtesting and experimentation. • Partner with the Product Engineering team to refine our Risk & Underwriting models, balancing the goal of maximizing cash advances with the need to maintain a balanced lending portfolio. • Collaborate with the Expert Pricing team and domain experts to encode market nuances into automated features and inferences, ensuring our models respect the qualitative factors collectors care about. • Define, monitor, and lift model performance metrics and communicate the "why" behind price coverage, movements, and freshness toward becoming the ultimate liquidity platform.

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description We are seeking an experienced Sr. Clinical Data Manager to lead and oversee data management activities for medical device clinical trials, with a focus on Electrophysiology (EP). This role involves ensuring high-quality, regulatory-compliant data collection and analysis to support clinical trials from start-up to submission. - Lead all aspects of data management for medical device clinical trials, ensuring compliance with FDA, MDR, ICH-GCP, and ISO 14155 regulations. - Develop and maintain Data Management Plans (DMPs) and Case Report Forms (CRFs) aligned with study protocols. - Design, validate, and manage Electronic Data Capture (EDC) systems such as Medidata RAVE, Oracle InForm, or Veeva Vault EDC. - Oversee data cleaning, query management, and reconciliation processes to ensure high-quality, analyzable datasets. - Ensure adherence to CDISC/CDASH/SDTM standards, enabling efficient regulatory submission. - Conduct and support data audits, quality control (QC) reviews, and database lock activities. - Serve as the data management lead for multiple studies, collaborating with cross-functional teams including clinical operations, biostatistics, and regulatory affairs. - Work closely with electrophysiology (EP) experts, principal investigators, and clinical research teams to ensure accurate data collection and interpretation. - Provide mentorship and guidance to junior data managers and other team members. - Manage relationships with CROs, vendors, and external data management teams, ensuring alignment with project timelines and quality standards. - Ensure compliance with FDA 21 CFR Part 11, ICH-GCP, ISO 14155 (for medical devices), and MDR regulations. - Support regulatory submissions (e.g., PMA, 510(k), CE Mark, IDE) by ensuring proper data documentation and formatting. - Participate in audits and inspections by regulatory bodies and implement corrective actions as needed. - Stay updated on evolving medical device data management best practices and regulatory requirements. - Generate and review clinical study reports, data listings, and reconciliation outputs. - Support statistical programming and data analysis in collaboration with biostatistics teams. - Ensure seamless data integration with clinical trial management systems (CTMS) and safety databases. Qualifications - Bachelor's or Master's degree in Life Sciences, Clinical Research, Biostatistics, Computer Science, or a related field. - Certified Clinical Data Manager (CCDM) or equivalent certification is a plus. - 5+ years of experience in clinical data management within the medical device industry or related healthcare sectors. - Proven experience in Phase I-IV clinical trials, IDE/PMA/510(k) submissions, or post-market studies. - Strong knowledge of electrophysiology (EP) trials is a plus but not required. - Experience managing EDC systems (e.g., Medidata RAVE, Veeva Vault, Oracle InForm, Medrio). - Familiarity with ISO 14155, CDISC/CDASH/SDTM, and FDA 21 CFR Part 11 compliance. - Knowledge of SQL, SAS, or other clinical data programming tools is beneficial. Requirements - Strong leadership, problem-solving, and analytical abilities. - Excellent attention to detail and ability to manage multiple clinical studies simultaneously. - Strong communication and collaboration skills for working with clinical, regulatory, and biostatistics teams. - Ability to work effectively with CROs, vendors, and external stakeholders. Location and Travel Requirements - This is a fully remote position and may be performed anywhere within the United States of America. - Occasional domestic travel may be required for company meetings, audits, etc.

Role Description To make the food people love, we need to make sure we feed our decision makers the right data and solutions at the right time, every time. As a Senior Data Scientist at General Mills, you will apply your strong expertise in machine learning, data mining, and information retrieval to design, prototype, and build next-generation advanced analytics engines and services. You will collaborate with product owners, business partners, and other Data Scientists to define the technical problem statement and the hypothesis to test. In addition, you will develop efficient and accurate analytical models that mimic business decisions. Key Responsibilities - Design & implementation of LLM-based agentic AI solutions for semi-structured data, covering problem framing, data curation, and evaluation across classification, entity extraction & summarization. - Develop high‑quality Agentic AI and RAG workflows, optimizing ingestion, chunking, embeddings, and vector indexing with established frameworks and stores to maximize retrieval performance. - Technically evaluate novel technology to help inform on the best effort/improvement tradeoffs. - Quickly learn and iterate with novel technologies/procedures, particularly but not exclusively around generative AI. - Implement appropriate generative AI techniques and technologies to solve business problems, including prompt and LLM fine-tuning, RAG, agentic workflows, and tool designs. - Help business stakeholders translate their objectives into measurable outcomes. - Apply critical thinking to frame business needs into statistical problems and develop solutions that balance technical completeness/complexity and timely value delivery. - Be a storyteller to explain the ‘why and how’ of your recommendations to build trust in your solutions. - Have passion to advocate and educate on the value and importance of data-driven decision-making and analytical methods. - Identify and develop long-term data science processes, frameworks, tools, and standards. - Be a part of the team, collaborate, ask questions, engage, and solicit feedback from other Data Scientists. Qualifications - Bachelor’s degree in data science, Computer Science, Engineering, Math, Statistics or other related fields. - 6+ years of relevant experience with at least 2 years of relevant experience in Data Science. - Strong foundation of statistical reasoning and critical thinking, with a proven track record of effectively finding solutions to stakeholder problem statements. - Experience with Python, including some exposure to package development and foundational programming principles. - Some experience with generative AI concepts, including embeddings, LLMs, agents, and RAG. - Experience writing complex SQL queries. - Understanding data warehousing and complex databases. - Experience with machine learning and data visualization tools. - Number sense, ability to identify questionable data, dig in and address it. - Strong communication and collaboration skills to understand business needs, frame problems in familiar language (without compromising statistical fidelity), and present solutions that clearly translate to business value. Preferred Qualifications - Experience developing and deploying Python packages and managing large-scale projects. - Experience developing/deploying generative AI technology, including agents, RAG, or similar. - Experience with Google Cloud Platform or similar technology. - Applied experience in consumer goods/supply chain. - Master’s degree in data science, Computer Science, Engineering, Math, or Statistics preferred. Additional Considerations - Applicants for this position must be currently authorized to work in the United States on a full-time basis, and General Mills will not sponsor applicants for this position for work visas. Salary Range The salary range for this position is $108900.00 - $181700.00 / Annually. At General Mills we strive for each employee's pay at any point in their career to reflect their experiences performance and skills for their current role. The salary range for this role represents the numerous factors considered in the hiring decisions including, but not limited to, educations, skills, work experience, certifications, etc. As such, pay for the successful candidate(s) could fall anywhere within the stated range. Beyond base salary, General Mills offers a competitive Total Rewards package focusing on your overall well-being. We are proud to offer a foundation of health benefits, retirement and financial wellbeing, time off programs, wellbeing support and perks. Benefits may vary by role, country, region, union status, and other employment status factors. You may also be eligible to participate in an annual incentive program. An incentive award, if any, depends on various factors, including, individual and organizational performance. Reasonable Accommodation Request If you need to request an accommodation during the application or hiring process, please fill out our online accommodations request form by following this link: Accommodations Request.

• Experimentation Strategy: Own the end-to-end testing framework. You will design, execute, and analyze A/B tests that define our growth playbook. • Growth Modeling: Build predictive models for user retention and LTV. You’ll identify the user behaviors that lead to long-term business growth. • Strategic Partnership: Act as a "Product Manager of Data," translating complex analytical findings into clear, high-stakes recommendations for consumer leadership. • Funnel Optimization: Deep-dive into the acquisition and onboarding funnels to identify friction points and opportunities for viral loops. • AI & Future-Proofing: Explore how AI and machine learning can be leveraged to personalize user experiences and automate growth workflows.