Healthcare Decision Scientist

About the Role

Every day, Cambia's dedicated team of Decision Scientists is living our mission to make health care easier and lives better. As a member of the Data & Analytics team, our Decision Scientists partner with business leaders to frame ambiguous challenges, apply advanced analytics and machine learning, and translate complex insights into clear, actionable recommendations – all in service of creating an economically sustainable health care system. Do you have the curiosity of a scientist, the pragmatism of a consultant, and the communication skills of a trusted advisor? Do you thrive on turning data into decisions that drive measurable business outcomes? Then this role may be the perfect fit.

Qualifications

• Decision Scientist II: Master's degree (PhD preferred) in a strongly quantitative field such as Statistics, Applied Mathematics, Physics, Operations Research, Computer Science, Bioinformatics, or Econometrics and 3+ years of progressive, related work experience. In lieu of an advanced degree, a BA/BS degree with 5 years of related work experience will be considered.
• Decision Scientist III: Master's degree (PhD preferred) in a strongly quantitative field such as Statistics, Applied Mathematics, Physics, Operations Research, Computer Science, Bioinformatics, or Econometrics and 5+ years of progressive, related work experience. In lieu of an advanced degree, a BA/BS degree with 8 years of related work experience will be considered.

Skills

• Ability to think creatively and demonstrate strong analytical skills, analyzing complex situations both independently and as part of a team, learning quickly and synthesizing solutions, options and action plans.
• Familiarity with mixed methods research, with experience in at least two of the following: advanced analytics (statistics, simulation, optimization, time series, longitudinal studies, life event modeling); data mining, NLP, machine learning (regression, clustering, neural networks, kernel methods, dimensionality reduction, ensemble methods, decision trees) and deep learning (CNN, LSTM, GAN); building solutions with tools for data mining, statistics, analysis and scripting (e.g., Python).
• Experience cleaning, aggregating, and pre-processing data from varied sources, and creating complex SQL queries for both standard and ad-hoc data mining purposes.
• Demonstrated ability to apply quantitative analysis techniques to business situations, including forecasting, descriptive statistics, statistical inference, and multivariate modeling.
• Ability to analyze and interpret qualitative data (research, feedback) and incorporate those insights into quantitative analyses.
• Proficiency with a range of business intelligence and analysis tools (R, Power BI, or Tableau) and at least one statistical/analytical programming tool (Python, R, etc.).
• Project experience with machine learning, data mining, and constructing analytical models and algorithms.
• Experience with fundamental software engineering practices (e.g., version control, unit testing, CI/CD).
• Ability to access and process structured and unstructured data in various databases and formats.
• Ability to coordinate cross-functionally and manage time and tasks effectively to drive solutions and resolve issues in a timely manner.
• Excellent oral and written communication skills to effectively interface with a broad array of internal and external contacts, including leadership.
• Additional for Decision Scientist II: Proven ability to provide analysis and data interpretation in support of strategy development, program implementation and evaluation; deep understanding of various machine learning algorithms and when to apply them; ability to maintain end-to-end ML models; ability to work with minimal direction; ability to distill informal requirements into problem definitions amid ambiguity; recognized as a technical leader by peers.
• Additional for Decision Scientist III: Ability to design systems across batch, streaming, and real-time processing; ability to evaluate cutting-edge ML research and frameworks; familiarity with semi-supervised, reinforcement, transfer, and graph-based learning; ability to simplify and improve the full ML model life cycle; strong facilitation and consensus-building skills; proven ability to influence others; ability to build ML pipelines through full ML cycles; ability to effectively lead teams without direct supervisory responsibility.
• Experience with AI tools and technologies to enhance productivity and decision-making in professional settings highly desired.

Responsibilities

• Design and execute analyses that uncover opportunities for business improvement and connect data, performance, and business strategy.
• Collaborate with stakeholders across sales, operational, clinical, finance, and provider functions to frame ambiguous challenges into structured analytical questions, identifying decision options, tradeoffs, risks, and expected outcomes.
• Select and apply appropriate analytical techniques (e.g., XGBoost, regression, clustering, forecasting, anomaly detection, causal analysis, experimental design) to develop predictive, descriptive, classification, segmentation, and optimization models that balance sophistication with practical applicability