Machine Learning Scientist (Future Opportunities) Remote Within OR, WA, UT or ID **Please note this role is a candidate pool, and we are always looking for top talent. We do not always have open positions but we encourage you to submit your resume so you will be considered for all open roles as they become available.** Primary Job Purpose Machine Learning Scientists (MLS) work with various stakeholders to design, develop, and implement data-driven solutions. This position applies expertise in advanced analytical tools such as machine learning, deep learning, optimization, and statistical modeling to solve business problems in the healthcare payer domain. Data Scientists in the Machine Learning track work may focus on a particular area of the business such as clinical care delivery, customer experience, or payment integrity, or they may work across several areas spanning the organization. In addition to expertise in analysis, machine learning and deep learning, this role requires knowledge of data systems, basic software development best practices, and algorithmic design. Machine Learning Scientists work closely with AI team members in the Product and Engineering tracks to collaboratively develop and deliver models and data-driven products. ML Scientists also collaborate and communicate with business partners to design and develop data-driven solutions to business problems and interpret and communicate results to technical and non-technical audiences. General Functions and Outcomes For MLS I: Note that these responsibilities are representative but not exhaustive. Higher level roles involve successively stronger degrees of initiative taking and innovation beyond the core responsibilities listed here. <ul><li>Researches, designs, develops, and implements data-driven models and algorithms using machine learning, deep learning, statistical, and other mathematical modeling techniques.</li><li>Trains and tests models, and develops algorithms to solve business problems.</li><li>Adheres to standard best-practices and establishes principled experimental frameworks for developing data-driven models.</li><li>Develops models and performs experiments and analyses that are replicable by others.</li><li>Uses open-source packages when appropriate to facilitate model development</li></ul><ul><li>Identifies, measures, analyzes, and visualizes drivers to explain model performance (e.g., feature importance, interpretability, bias and error analysis), both offline (in the development phase) and online (in production).</li><li>Uses appropriate metrics and quantified outcomes to drive model and algorithm improvements.</li><li>Analyzes, diagnoses, and resolves bugs in production machine learning models and systems.</li><li>Evaluates model/use case feasibility by quickly generating prototypes.</li><li>Takes models from prototype stage and improves performance as needed.</li><li>Writes clean, well-commented, tested, version-controlled, and maintainable python code.</li><li>Collaborates with team members and Cambia business partners.</li><li>Actively participates in group meetings and discussions.</li><li>Communicates effectively both orally and in writing with both technical and non-technical audiences.</li><li>Keeps current with the state of the art in machine learning and AI and its application to healthcare.</li><li>Keeps current with evolving commercial and open source tools, techniques, and brings these practices to projects.</li><li>Over time develops familiarity and insight with various subdomains of healthcare data</li></ul> Additional General Functions & Outcomes for Machine Learning Scientist II <ul><li>Generates new features, by following examples, using SQL or SQL-like code.</li><li>Works effectively with data that may be noisy, high dimensional, sparse and/or imbalanced.</li><li>Contributes to the full life-cycle of modeling, from training, to model evaluation, to model deployment.</li><li>Builds robust production grade machine learning pipelines using tools and patterns in the AI platform.</li><li>Accesses and processes structured and unstructured data in various databases and formats.</li><li>Assesses new machine learning capabilities and adapts them to our problems and environment.</li><li>Identifies or develops appropriate model metrics and objective functions to ensure models satisfy stated business requirements and KPIs.</li><li>Proactively identifies potential pitfalls and risks and develops ways to avoid them.</li><li>Plays a role in ensuring that the work being carried out by the AI team has sufficient business value.</li></ul><ul><li>Writes clean, well-commented, efficient Python code.</li></ul> Additional General Functions & Outcomes for Machine Learning Scientist Sr. I <ul><li>Generates feature pipelines and datasets using novel data sources, where example SQL may not exist.</li><li>Works with large datasets that require cloud resources or distributed computing.</li><li>Takes on net new/greenfield work and establishes project foundations.</li><li>Identifies and determines what data is available and relevant, including both internal and external data sources.</li><li>Tech lead for 1-2 project teams, products, or end-to-end systems.</li><li>Builds reusable machine learning components, pipelines and tools for others to use.</li><li>Oversees technical output in their area(s)/domain(s).</li><li>Researches and evaluates possible solutions, including but not limited to vendors, open-source pre-trained models, cloud services, etc.</li><li>Stays abreast of novel approaches and techniques in the field. Shares new ideas and discoveries with the team and applies new ideas when appropriate to generate business value.</li><li>Devises project plans to address business problems, drawing from an increasingly broad ML toolset grounded in hands-on experiences and prior successes delivering business value.</li><li>In collaboration with Data Science Product Manager(s), develops target success criteria.</li><li>Participates in recruitment efforts by the team.</li><li>Mentors others to help develop their skills.</li></ul> Additional General Functions & Outcomes for Machine Learning Scientist Sr. II <ul><li>Contributes to the development of technical strategy and roadmaps in at least one major area of the AI portfolio.</li><li>Leads technical aspects of large-scale initiatives.</li></ul><ul><li>Makes material improvements in processes or infrastructure that reduces the load on team members.</li><li>Develops materials (oral and written) within the Cambia community to increase awareness of AI/ML and how it can be applied in the healthcare payer space.</li><li>Connects new machine learning opportunities and existing efforts to corporate and departmental strategies.</li><li>Represents Cambia through publishing research and acquiring patents.</li><li>Designs online experiments and assessment methods, for example A/A and A/B testing.</li><li>Identifies industry trends in machine learning/healthcare and communicates the impact and opportunities to the team and business leaders.</li><li>Works and interacts across the organization with a variety of business units.</li><li>Identifies team learning objectives and builds curricula.</li><li>Contributes to development of recruitment strategies for the AI team.</li></ul> Minimum Requirements Competencies and Knowledge: For all levels: <ul><li>Demonstrated knowledge of data science, machine learning, and modeling.</li><li>Ability to use well-understood techniques and existing patterns to build, analyze, deploy, and maintain models.</li><li>Effective in time and task management.</li><li>Able to develop productive working relationships with colleagues and business partners.</li><li>Strong interest in the healthcare industry.</li></ul> Core Knowledge: <ul><li>Machine Learning: Strong mathematical foundation and theoretical grasp of the concepts underlying machine learning, optimization, etc. (see below). Demonstrated understanding of how to structure simple machine learning pipelines (e.g., has prepared datasets, trained and tested models end-to-end).</li><li>Data: Strong foundation in data analysis.</li><li>Programming: Strong python programming skills. Familiarity with standard data science packages. Familiarity with standard software development best practices. Strong SQL skills a plus.</li><li>Algorithms: Understanding of standard algorithms and data structures (ex. search & sort) and their analysis.</li></ul> Additional Minimum Requirements for Machine Learning Scientist II <ul><li>Ability to code effectively in and create novel features and datasets using SQL or SQL-like languages.</li><li>Ability to write clean, well-commented, efficient Python code.</li><li>Strong understanding of techniques for working with noisy, high-dimensional, sparse, and/or imbalanced data.</li><li>Demonstrates in-depth familiarity with at least one domain of data (e.g., claims data).</li><li>Demonstrates depth of understanding in at least one major ML modeling technique or approach.</li><li>Ability to develop new machine learning pipelines for both offline testing and online serving of models.</li><li>Demonstrated track record of delivery of data science and machine learning models to solve well-defined business problems.</li><li>Has working knowledge of department processes, procedures, and infrastructure.</li><li>Able to identify common pitfalls in developing ML models (e.g., data leakage across features or partitions)</li><li>Ability to translate business requirements into data science and machine learning discovery plans and modeling objectives.</li><li>Ability to articulate the high-level business objectives of their work.</li><li>Performs a range of data science tasks with a moderate level of guidance and direction.</li><li>Ability to partner within and across departments to remove blocks and achieve results.</li></ul> Additional Minimum Requirements for Machine Learning Scientist Sr. I <ul><li>Advanced SQL or SQL-like and Python coding skills.</li><li>Ability to use novel techniques to solve challenging business problems.</li><li>Demonstrates in-depth familiarity with at least two domains of data.</li><li>Demonstrated ability to work with large datasets in a distributed computing environment.</li><li>Demonstrates independent judgment in selecting methods and techniques for delivering results.</li><li>Demonstrates expert-level technical skills in ML models and algorithms, and ensures technical quality of models and algorithms developed by others.</li><li>Able to partner with business stakeholders on ill-defined or improperly defined goals, extract underlying needs, and develop a discovery plan.</li><li>Able to quickly research and evaluate possible solutions to complex business problems.</li><li>Able to lead projects or sub-projects within a broader product.</li><li>Able to influence strategy and prioritization within their area(s).</li><li>Able to assist and mentor less-experienced data scientists.</li><li>Able to develop project plans.</li><li>Normally receives little instruction on day-to-day work, general instructions on new assignments.</li></ul> Additional Minimum Requirements for Machine Learning Scientist Sr. II <ul><li>Able to identify common patterns, and develop common tools and patterns to facilitate rapid experimentation, modeling, and development.</li><li>Broad and comprehensive, but also technically-deep awareness of data science & machine learning advancements in the field.</li><li>Demonstrated ability to influence strategy and prioritization.</li><li>Ability to design online experiments and assessment methods.</li><li>Experience advocating for AI/ML and developing a data-driven culture at an organization.</li><li>Ability to develop training/workshop materials.</li><li>Ability to communicate effectively across a large organization.</li><li>Demonstrated track record of contributions to the field, e.g., through publications, blog posts, or patents.</li></ul> Normally to be proficient in the competencies listed above: Machine Learning Scientist I would have a degree (masters or PhD preferred) in a strongly quantitative field such as Computer Science, Statistics, Applied Mathematics, Physics, Operations Research, Bioinformatics, or Econometrics, and 0-3 years of related work experience, or equivalent combination of education and experience. Machine Learning Scientist II would have a degree (masters or PhD preferred) in a strongly quantitative field such as Computer Science, Statistics, Applied Mathematics, Physics, Operations Research, Bioinformatics, or Econometrics, and typically requires 4 years of related work experience. Equivalent combination of education and experience will be considered. Machine Learning Scientist Sr I would have a degree (masters or PhD preferred) in a strongly quantitative field such as Computer Science, Statistics, Applied Mathematics, Physics, Operations Research, Bioinformatics, or Econometrics, and typically requires at least 7 years of related work experience. Equivalent combination of education and experience will be considered. Machine Learning Scientist Sr II would have a degree (masters or PhD preferred) in a strongly a strongly quantitative field such as Computer Science, Statistics, Applied Mathematics, Physics, Operations Research, Bioinformatics, or Econometrics, and typically at least 12 years of related work experience. Equivalent combination of education and experience will be considered. Work Environment Work primarily performed in an office environment and working from home may be considered. Travel may be required, locally or out of state. May be required to work overtime and outside of normal hours. The expected hiring range for a MLS I is $109,700 - $148,400, for a MLS II is $119,900 - $162,200, for a Sr MLS I is $145,400 - $196,700 and for a Sr MLS II is $176,000 - $238,100 depending on skills, experience, education, and training; relevant licensure / certifications; performance history; and work location. The bonus target for a MLS I and MLS II is 15% & the bonus target for a Sr MLS I and Sr MLS II is 20%. The current full salary range for this role is $103,000 - $168,000 for MLS I, is $112,000 - $184,000 for MLS II, is $126,000 - $223,00 for Sr MLS I, and is $165,000 - $270,000 for Sr MLS II. Base pay is just part of the compensation package at Cambia that is supplemented with an exceptional 401(k) match, bonus opportunity and other benefits. In keeping with our Cause and vision, we offer comprehensive well-being programs and benefits, which we periodically update to stay current. Some highlights: <ul><li>medical, dental, and vision coverage for employees and their eligible family members</li><li>annual employer contribution to a health savings account ($1,200 or $2,500 depending on medical coverage, prorated based on hire date)</li><li>paid time off varying by role and tenure in addition to 10 company holidays</li><li>up to a 6% company match on employee 401k contributions, with a potential discretionary contribution based on company performance (no vesting period)</li><li>up to 12 weeks of paid parental time off (eligible day one of employment if within first 12 months following birth or adoption)</li><li>one-time furniture and equipment allowance for employees working from home</li><li>up to $225 in Amazon gift cards for participating in various well-being activities. for a complete list see our External Total Rewards page.</li></ul> We are an Equal Opportunity and Affirmative Action employer dedicated to workforce diversity and a drug and tobacco-free workplace. All qualified applicants will receive consideration for employment without regard to race, color, national origin, religion, age, sex, sexual orientation, gender identity, disability, protected veteran status or any other status protected by law. A background check is required. If you need accommodation for any part of the application process because of a medical condition or disability, please email CambiaCareers@cambiahealth.com. Information about how Cambia Health Solutions collects, uses, and discloses information is available in our Privacy Policy. As a health care company, we are committed to the health of our communities and employees during the COVID-19 pandemic. Please review the policy on our Careers site.

Senior Machine Learning Scientist Remote within OR, WA, ID or UT Cambia Health Solutions is working to create a seamless and frictionless healthcare experience for consumers nationwide. This presents a unique challenge and opportunity for innovative solutions that serve patients and providers and influence the healthcare system. Cambia's Applied AI team builds, prototypes, and deploys data-driven models and algorithms to production systems, delivering more equitable, effective, and affordable health care to our members.We are seeking a highly-skilled and experienced Senior Machine Learning Scientist to join us and help advance our current and future work applying machine learning, deep learning, and NLP to deliver better health care. We contribute broadly across Cambia, working on a wide range of challenging problems. In this role, we are seeking a technical leader for our Healthcare Services Operations AI team working on use cases such as Streamline prior authorization and appeal reviews using GenAI and NLP. Summarizing medical policy decisions in plain language for our members. Identifying members at risk for certain conditions to offer them opportunities for high-value care. And much more!As a Senior ML Scientist on our team, you will play a crucial role in leading the team in understanding requirements, prototyping and building models, conducting experiments, and driving innovative solutions. Your passion for machine learning, deep learning, and NLP, coupled with your curiosity and desire to keep learning, will be instrumental in advancing Cambia's data-driven initiatives. In collaboration with your fellow ML Scientists, our AI product team, and our partner data engineering and software development teams, you will bring ML models to production and maintain their health and integrity while in production. Your expertise in theoretical machine learning, coupled with your practical experience engineering ML systems, will be instrumental in driving the success of Cambia's AI/ML initiatives.Qualifications and Requirements Academic degree (masters or PhD preferred) in Computer Science, Engineering, or a related field. Minimum of 7 years of experience in ML development and deploying ML solutions in cloud-based production environments for a Senior MLS I Minimum of 9-12 years of experience in ML development and deploying ML solutions in cloud-based production environments for a Senior MLS II Machine learning: Strong mathematical foundation and understanding of the concepts underlying classic machine learning, deep learning, NLP, statistical modeling, and data analysis. Expert-level familiarity with common machine learning frameworks and libraries such as TensorFlow, PyTorch, scikit-learn, XGBoost, etc. Natural Language Processing (NLP): Expertise in NLP and experience using NLP libraries like NLTK, SpaCy, Gensim, etc. is a strong plus. Generative AI and Large Language models (strongly preferred): Understanding and experience working with language (LLM) models and generative AI, including encoder-only (BERT Family) and autoregressive models (GPT family). Experience using different frameworks and libraries including but not limited to huggingface, Langchain, Llamaindex, vector databases. Model development and evaluation: Experience in and deep intuition for applying ML techniques and approaches to solve problems. Strong foundation in model evaluation, including metric development and selection. Software development skills: Solid understanding of software engineering principles, data structures, and algorithms. Expert-level proficiency in Python. ML production systems: Experience deploying ML models in production systems. Exposure to containerization technologies (e.g., Docker, Kubernetes) and cloud platforms (e.g., AWS, Azure, GCP) is helpful. Strong grounding in model monitoring and MLOps. Data preprocessing and analysis: Understanding of how to structure machine learning pipelines. Familiarity with data preprocessing techniques and tools. Experience with SQL and/or python data processing libraries (e.g., Pandas, NumPy). Analytical mindset: Strong analytical thinking and problem-solving abilities to contribute to data analysis and experimental evaluations. Attention to detail and an eagerness to learn from experimental results. Communication and teamwork: Good communication skills to collaborate effectively with cross-functional teams. Willingness to collaborate and learn with team members. Continuous learning: Passion for staying up-to-date with the latest advancements in machine learning and data engineering. Proactive in learning new tools, techniques, and frameworks to drive innovation. Leadership: Strong leadership, coaching, and mentorship abilities. Ability to inspire and motivate team members towards achieving goals and delivering high-quality results. Responsible AI: Desire to adhere to ethical considerations and responsible AI practices in machine learning. Familiarity with fairness, bias mitigation, privacy, and security aspects of machine learning models. Healthcare knowledge: Previous experience is extremely helpful but not required.Responsibilities: Technical leadership: Lead projects and mentor machine learning scientists and engineers. Provide technical guidance and model technical excellence. Foster a collaborative and inclusive team culture that encourages innovation, growth, and continuous learning. Requirement analysis and solution design: Collaborate with cross-functional teams to understand business requirements, define clear objectives, and develop technical plans. Work with stakeholders to identify opportunities where machine learning techniques can provide valuable insights and solutions. Data preprocessing and feature engineering: Implement robust and reusable data preprocessing and feature engineering pipelines to extract meaningful insights from raw data. Clean, transform, and prepare datasets to facilitate effective model training and evaluation. Model prototyping and development: Use machine learning, deep learning, and NLP to prototype, develop, and refine models on top of our ML platform, leveraging best practices and established frameworks. Implement algorithms and techniques to meet requirements and objectives of specific business problems. Experimentation and evaluation: Conduct experiments and evaluations to assess the performance and effectiveness of different models and techniques. Develop metrics that reflect the needs of the business for their use cases. Analyze experimental results, interpret findings, and provide actionable recommendations. Model deployment and productionization: Work with AI Engineers to optimize and adapt models for real-time, scalable, and efficient performance. Collaborate with engineering and infrastructure teams to ensure seamless integration and deployment of models into production systems. Model monitoring and maintenance: Track the performance and impact of machine learning models and solutions in production settings. Report on KPIs and provide actionable insights to stakeholders. Continuously iterate and improve models based on feedback and real-world data. Continuous learning and innovation: Stay updated with the latest advancements in machine learning, deep learning, and NLP, particularly as applied in healthcare. Explore and evaluate new algorithms, frameworks, and tools to enhance model performance and efficiency. Machine learning strategy: Contribute to and execute the organization's machine learning strategy. Identify and weigh in on areas where machine learning can provide the most value and competitive advantage. Compliance and ethical considerations: Ensure compliance with Responsible AI guidelines in the development and deployment of machine learning models. Promote fairness, transparency, and accountability in all aspects of machine learning initiatives.Work Environment Work primarily performed in a hybrid environment consisting of in-office and working from home. Travel may be required, locally or out of state.

Senior Machine Learning Scientist Remote within OR, WA, ID or UT Cambia Health Solutions is working to create a seamless and frictionless healthcare experience for consumers nationwide. This presents a unique challenge and opportunity for innovative solutions that serve patients and providers and influence the healthcare system. Cambia's Applied AI team builds, prototypes, and deploys data-driven models and algorithms to production systems, delivering more equitable, effective, and affordable health care to our members.We are seeking a highly-skilled and experienced Senior Machine Learning Scientist to join us and help advance our current and future work applying machine learning, deep learning, and NLP to deliver better health care. We contribute broadly across Cambia, working on a wide range of challenging problems. In this role, we are seeking a technical leader for our Healthcare Services Operations AI team working on use cases such as Streamline prior authorization and appeal reviews using GenAI and NLP. Summarizing medical policy decisions in plain language for our members. Identifying members at risk for certain conditions to offer them opportunities for high-value care. And much more!As a Senior ML Scientist on our team, you will play a crucial role in leading the team in understanding requirements, prototyping and building models, conducting experiments, and driving innovative solutions. Your passion for machine learning, deep learning, and NLP, coupled with your curiosity and desire to keep learning, will be instrumental in advancing Cambia's data-driven initiatives. In collaboration with your fellow ML Scientists, our AI product team, and our partner data engineering and software development teams, you will bring ML models to production and maintain their health and integrity while in production. Your expertise in theoretical machine learning, coupled with your practical experience engineering ML systems, will be instrumental in driving the success of Cambia's AI/ML initiatives.Qualifications and Requirements Academic degree (masters or PhD preferred) in Computer Science, Engineering, or a related field. Minimum of 7 years of experience in ML development and deploying ML solutions in cloud-based production environments for a Senior MLS I Minimum of 9-12 years of experience in ML development and deploying ML solutions in cloud-based production environments for a Senior MLS II Machine learning: Strong mathematical foundation and understanding of the concepts underlying classic machine learning, deep learning, NLP, statistical modeling, and data analysis. Expert-level familiarity with common machine learning frameworks and libraries such as TensorFlow, PyTorch, scikit-learn, XGBoost, etc. Natural Language Processing (NLP): Expertise in NLP and experience using NLP libraries like NLTK, SpaCy, Gensim, etc. is a strong plus. Generative AI and Large Language models (strongly preferred): Understanding and experience working with language (LLM) models and generative AI, including encoder-only (BERT Family) and autoregressive models (GPT family). Experience using different frameworks and libraries including but not limited to huggingface, Langchain, Llamaindex, vector databases. Model development and evaluation: Experience in and deep intuition for applying ML techniques and approaches to solve problems. Strong foundation in model evaluation, including metric development and selection. Software development skills: Solid understanding of software engineering principles, data structures, and algorithms. Expert-level proficiency in Python. ML production systems: Experience deploying ML models in production systems. Exposure to containerization technologies (e.g., Docker, Kubernetes) and cloud platforms (e.g., AWS, Azure, GCP) is helpful. Strong grounding in model monitoring and MLOps. Data preprocessing and analysis: Understanding of how to structure machine learning pipelines. Familiarity with data preprocessing techniques and tools. Experience with SQL and/or python data processing libraries (e.g., Pandas, NumPy). Analytical mindset: Strong analytical thinking and problem-solving abilities to contribute to data analysis and experimental evaluations. Attention to detail and an eagerness to learn from experimental results. Communication and teamwork: Good communication skills to collaborate effectively with cross-functional teams. Willingness to collaborate and learn with team members. Continuous learning: Passion for staying up-to-date with the latest advancements in machine learning and data engineering. Proactive in learning new tools, techniques, and frameworks to drive innovation. Leadership: Strong leadership, coaching, and mentorship abilities. Ability to inspire and motivate team members towards achieving goals and delivering high-quality results. Responsible AI: Desire to adhere to ethical considerations and responsible AI practices in machine learning. Familiarity with fairness, bias mitigation, privacy, and security aspects of machine learning models. Healthcare knowledge: Previous experience is extremely helpful but not required.Responsibilities: Technical leadership: Lead projects and mentor machine learning scientists and engineers. Provide technical guidance and model technical excellence. Foster a collaborative and inclusive team culture that encourages innovation, growth, and continuous learning. Requirement analysis and solution design: Collaborate with cross-functional teams to understand business requirements, define clear objectives, and develop technical plans. Work with stakeholders to identify opportunities where machine learning techniques can provide valuable insights and solutions. Data preprocessing and feature engineering: Implement robust and reusable data preprocessing and feature engineering pipelines to extract meaningful insights from raw data. Clean, transform, and prepare datasets to facilitate effective model training and evaluation. Model prototyping and development: Use machine learning, deep learning, and NLP to prototype, develop, and refine models on top of our ML platform, leveraging best practices and established frameworks. Implement algorithms and techniques to meet requirements and objectives of specific business problems. Experimentation and evaluation: Conduct experiments and evaluations to assess the performance and effectiveness of different models and techniques. Develop metrics that reflect the needs of the business for their use cases. Analyze experimental results, interpret findings, and provide actionable recommendations. Model deployment and productionization: Work with AI Engineers to optimize and adapt models for real-time, scalable, and efficient performance. Collaborate with engineering and infrastructure teams to ensure seamless integration and deployment of models into production systems. Model monitoring and maintenance: Track the performance and impact of machine learning models and solutions in production settings. Report on KPIs and provide actionable insights to stakeholders. Continuously iterate and improve models based on feedback and real-world data. Continuous learning and innovation: Stay updated with the latest advancements in machine learning, deep learning, and NLP, particularly as applied in healthcare. Explore and evaluate new algorithms, frameworks, and tools to enhance model performance and efficiency. Machine learning strategy: Contribute to and execute the organization's machine learning strategy. Identify and weigh in on areas where machine learning can provide the most value and competitive advantage. Compliance and ethical considerations: Ensure compliance with Responsible AI guidelines in the development and deployment of machine learning models. Promote fairness, transparency, and accountability in all aspects of machine learning initiatives.Work Environment Work primarily performed in a hybrid environment consisting of in-office and working from home. Travel may be required, locally or out of state.by Jobble

Machine Learning Scientist II Remote or Hybrid or On-site within OR, WA, ID or UT Cambia Health Solutions is working to create a seamless and frictionless health care experience for consumers nationwide. This presents a unique challenge and opportunity for innovative solutions that serve patients and providers and influence the healthcare system. Cambia's AI team builds, prototypes, and deploys data-driven models and algorithms to production systems, delivering more equitable, effective, and affordable health care to our members.We are seeking a talented and skilled Machine Learning Scientist II to join us and help advance our current and future work applying machine learning, deep learning, and NLP to deliver better health care. We contribute broadly across Cambia, working on a wide range of challenging problems, for instance:<ul><li>Reducing our members' claim costs using both supervised and unsupervised approaches.</li><li>Speeding up prior authorizations and appeals using NLP to understand clinical notes.</li><li>Personalizing member engagement to promote the health and well-being of our members.</li><li>Driving health equity across Cambia initiatives.</li><li>And much more!</li></ul>As a Machine Learning Scientist II, you will play a vital role in understanding requirements, prototyping and building models, conducting experiments, and driving innovative solutions. Your passion for machine learning, deep learning, and NLP, coupled with your eagerness to learn and grow, will be instrumental in advancing Cambia's data-driven initiatives.Qualifications & Requirements:<ul><li>Academic degree (master's or PhD preferred) in Data Science, Computer Science, Statistics, or a related field.</li><li>Machine learning: Strong mathematical foundation and understanding of the concepts underlying machine learning, deep learning, NLP, statistical modeling, and data analysis. Familiarity with common machine learning frameworks and libraries such as TensorFlow, PyTorch, scikit-learn, XGBoost, etc. Understanding of standard algorithms (e.g., search & sort) and data structures, and their analysis.</li><li>NLP: Expertise in NLP and experience using LLM and NLP libraries like NLTK, spaCy, or Hugging Face transformers is a plus.</li><li>Model development and evaluation: Experience applying a variety of ML techniques and approaches to solve problems. Strong foundation in model evaluation, including metric development and selection.</li><li>Familiarity with production systems: Basic understanding of software engineering principles and considerations for deploying ML models in production systems. Exposure to containerization technologies (e.g., Docker, Kubernetes) and cloud platforms (e.g., AWS, Azure, GCP) is helpful. Understanding of model monitoring and MLOps.</li><li>Data preprocessing and analysis: Understanding of how to structure machine learning pipelines. Familiarity with data preprocessing techniques and tools. Experience with SQL and/or python data processing libraries (e.g., Pandas, NumPy).</li><li>Analytical mindset: Strong analytical thinking and problem-solving abilities to contribute to data analysis and experimental evaluations. Attention to detail and an eagerness to learn from experimental results.</li><li>Communication and teamwork: Good communication skills to collaborate effectively with cross-functional teams. Willingness to collaborate and learn with team members.</li><li>Healthcare knowledge: Previous experience is beneficial but not required.</li></ul> Responsibilities: <ul><li>Model prototyping and development: Use machine learning, deep learning, and NLP to prototype, develop, and refine models on top of our ML platform, leveraging best practices and established frameworks. Implement algorithms and techniques to meet requirements and objectives of specific business problems.</li><li>Experimentation and evaluation: Conduct experiments and evaluations to assess the performance and effectiveness of different models and techniques. Develop metrics that reflect the needs of the business for their use cases. Analyze experimental results, interpret findings, and provide actionable recommendations.</li><li>Model deployment and productionization: Work with ML Engineers to optimize and adapt models for real-time, scalable, and efficient performance. Collaborate with engineering and infrastructure teams to ensure seamless integration and deployment of models into production systems.</li><li>Requirement analysis and solution design: Collaborate with cross-functional teams to understand business requirements, define clear objectives, and develop technical plans. Work with stakeholders to identify opportunities where machine learning techniques can provide valuable insights and solutions.</li><li>Data preprocessing and feature engineering: Implement robust and reusable data preprocessing and feature engineering pipelines to extract meaningful insights from raw data. Clean, transform, and prepare datasets to facilitate effective model training and evaluation.</li><li>Continuous learning and innovation: Stay updated with the latest advancements in machine learning, deep learning, and NLP, particularly as applied in healthcare. Explore and evaluate new algorithms, frameworks, and tools to enhance model performance and efficiency.</li></ul> Work Environment: <ul><li>Work primarily performed in a hybrid environment consisting of in-office and working from home. </li><li>Travel may be required, locally or out of state. </li></ul>by Jobble

Machine Learning Scientist Jobs in Portland, OR

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Machine Learning Scientist Jobs in Portland, OR

Machine Learning ScientistApril 23, 2024

Machine Learning ScientistApril 20, 2024

Senior Machine Learning ScientistApril 19, 2024

Senior Machine Learning ScientistApril 18, 2024

Machine Learning Scientist IiApril 18, 2024

Machine Learning ScientistApril 15, 2024

Senior Machine Learning ScientistApril 15, 2024

Senior Machine Learning ScientistApril 12, 2024

Senior Machine Learning ScientistApril 11, 2024