This page tracks remote data scientist openings that are location-eligible for Colorado.
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Access. Answers. Advocacy. We're raising the standard of healthcare for everyone.
• Drive insight generation for complex projects both independently and in collaboration with cross-functional teams. • Develop models of cost and quality in support of our navigation and match services • Identify opportunities to better engage and navigate members so that we can drive top quartile outcomes at bottom quartile costs. • Develop a deep understanding of how our products are being used and identify opportunities to better serve our members’ needs. • Prototype novel approaches to measuring or inferring product and business performance. • Scale our impact so we can bring our services to more people in need.
Role Description The Senior Data Scientist is a strategic leader in our organization, driving the entire lifecycle of data science initiatives that directly impact healthcare outcomes. Leveraging your deep expertise and mastery of machine learning, you will spearhead the development, implementation, and evaluation of complex AI models in healthcare settings specifically population health. Your ability to translate technical concepts into actionable insights will empower stakeholders to make informed decisions that enhance patient care and operational efficiency. You will also play a crucial role in mentoring and developing junior data scientists and analysts, fostering a culture of data-driven innovation. - Leads and manages the entire lifecycle of data science projects, from conceptualization and design to development, deployment, and ongoing optimization. - Build and deploy advanced analytics that explain and predict acute utilization (Inpatient/Emergency Department) and quantify how care delivery changes impact outcomes for heart failure and other high-risk populations. - Translate longitudinal patient care data into actionable intervention points across primary care, specialty care, and monitoring programs. - Partner with clinical and operational leaders to convert analytic findings into care pathway recommendations, operational triggers, and monitoring protocols; define measures of success and evaluate impact. - Collaborate with cross-functional teams to define project scope, objectives, analytic design, validation strategy, and expected impact, ensuring alignment with organizational goals and measurable improvements in healthcare outcomes. - Leverages deep understanding of machine learning algorithms to build patient-level and population-level models that support risk stratification, trajectory analysis, forecasting, capacity planning, and scenario analysis for diverse healthcare applications. - Utilizes clustering, dimension reduction, and deep generative models to uncover hidden patterns and insights within large, complex healthcare datasets. - Applies rigorous validation techniques to ensure model accuracy, stability, fairness, generalizability, and clinical usefulness across patient cohorts, sites, time periods, and operational settings. - Oversees the deployment of models into production environments, ensuring seamless integration with existing systems. - Extracts insights from clinical and operational data sources (Epic Clarity, HL7, and other enterprise data sources) to inform decision-making and guide project direction. - Translates complex technical findings into compelling narratives that resonate with non-technical stakeholders through presentations, dashboards, technical documentation, and stakeholder discussions. - Facilitates data-driven decision-making by effectively communicating the value and impact of AI models. - Mentors and guide junior data scientists, fostering their professional growth and technical expertise. - Promotes a culture of collaboration, knowledge sharing, and continuous learning within the data science team. - Contributes to developing best practices and standards for data science and machine learning within the organization. - Stays abreast of the latest advancements in machine learning and healthcare research to identify opportunities for improvement and innovation. - Experiments with new approaches and technologies to enhance model performance and expand the organization's data science capabilities. Qualifications - Preferred skills: Databricks, Python, SQL, advanced statistical analysis, machine learning, emerging AI technologies and implementation (LLMs, RAG, GenAI, Agentic workflow integrations and deployment). - Healthcare experience preferably with Population Health initiatives. - Familiarity with Epic Clarity, Caboodle, claims data, CMS/Medicare populations, or payer-provider analytics. - Education: Bachelor's Degree-Related Field of Study (Required). - Experience: Minimum of 4 years-Relevant experience (Required). Requirements - Analytical Thinking - C++ Programming Language - Clinical Data Cleaning - Communication - Group Collaboration - Machine Learning Methods - Python (Programming Language) - Statistical Methods - Structured Query Language (SQL) Benefits - Healthcare benefits for full time and part time positions from day one, including vision, dental and domestic partners. - Encouragement of an atmosphere of collaboration, cooperation and collegiality.
• Lead the design, development, deployment, and optimization of machine learning, predictive analytics, and AI-powered solutions. • Translate business challenges and opportunities into analytical approaches, model specifications, and measurable success criteria. • Apply advanced statistical analysis, machine learning techniques, and data science methodologies to solve complex business problems. • Analyze large, complex datasets to identify trends, patterns, opportunities, and actionable insights. • Develop and maintain model documentation, technical specifications, and implementation plans. • Stay current with emerging technologies, tools, and best practices in data science, machine learning, and artificial intelligence. • Design and execute comprehensive validation and evaluation strategies for machine learning and generative AI solutions. • Develop benchmarking frameworks and success metrics to assess model performance, reliability, and business impact. • Evaluate model quality using quantitative and qualitative measures, including accuracy, precision, recall, robustness, latency, and business outcome metrics. • Assess generative AI applications for response quality, grounding, relevance, consistency, and hallucination risk. • Identify and mitigate risks related to bias, fairness, explainability, privacy, and model reliability. • Perform model validation, testing, and performance assessments prior to production deployment. • Establish monitoring processes and evaluation methodologies to ensure continued model effectiveness and alignment with business objectives. • Design, execute, and analyze experiments, including A/B tests and statistical studies, to measure product and business outcomes. • Define key performance indicators and success metrics for machine learning and AI initiatives. • Measure and communicate the impact of analytical solutions through statistical analysis and quantitative methods. • Partner with stakeholders to define hypotheses, success criteria, and decision-making frameworks. • Use experimentation and data-driven insights to guide product, operational, and strategic decisions. • Collaborate with Engineering and Data Engineering teams to implement, operationalize, and scale models in production environments. • Monitor deployed models for performance degradation, model drift, data quality issues, and changing business conditions. • Recommend retraining, optimization, or replacement strategies based on model performance and evolving business needs. • Support the creation of scalable, maintainable, and reliable AI and machine learning solutions. • Ensure model deployment processes align with engineering best practices and operational requirements. • Partner with Product, Engineering, Analytics, and business stakeholders to prioritize opportunities and deliver high-impact solutions. • Communicate complex analytical findings and technical concepts to both technical and non-technical audiences. • Present recommendations, insights, and model performance results to leadership and project teams. • Support technical reviews, project planning, and delivery activities across cross-functional initiatives. • Contribute to knowledge sharing, documentation, and best practices within the data science organization. • Provide technical guidance and mentorship to junior team members and peers as needed.
Celerion Can Take You from First-In-Human through Proof-of-Concept.
• Deliver comprehensive data management services across all study phases • Independently own assigned data management studies and deliverables from startup through database lock, proactively identifying risks, resolving issues, communicating status, and escalating when appropriate • Ensure clinical databases are complete, accurate, and compliant with Sponsor and regulatory standards • Serve as primary Sponsor contact for data management activities • Lead data management communications and coordinate with internal and external teams to ensure timely delivery of study milestones and progress updates • Train site staff (CRCs, CRAs, PIs) and client teams on EDC systems • Oversee CRF lifecycle from design to final delivery • Conduct User Acceptance Testing (UAT) and ensure database setup aligns with specifications • Develop and manage essential study documents (e.g., Data Management Plan, CRF Completion Guidelines, Validation Plans, SAE Reconciliation Plans) • Review and clean clinical data, manage queries, and reconcile third-party data • Coordinate database lock and final data delivery • Identify risks and proactively resolve project issues • Provide exceptional service to internal and external stakeholders
Chemistry is the key to drug discovery, but chemical data is stuck in the twentieth century. We’re generating precise chemical datasets purpose-built to propel drug discovery into the information age — and we’re doing it on a larger scale and faster than has ever before been possible. Terray Therapeutics is a biotechnology company led by pioneers and long-time leaders in artificial intelligence, synthetic (medicinal) chemistry, biology and preclinical development, automation, and nanotechnology. Chemical datasets generated using our novel ultra-dense microarray technology work seamlessly with our integrated machine learning and computational platform to systematically map biochemical interactions between small molecules and causes of disease. Iterative cycles of virtual molecular design and experimentation power AI and machine learning models, which in turn guide the next cycle of design. With a chemistry engine that measures billions of interactions daily and becomes increasingly precise with every cycle, we can answer an unprecedented array of questions — deriving insights that enable us to predictably create drugs for patients in need.
Role Description Terray Therapeutics is seeking a highly motivated and experienced in vivo scientist with expertise in models of autoimmunity and inflammation to join our Translational Biology team. In this role, the candidate will serve as a scientific and operational lead for outsourced in vivo pharmacology and nonclinical safety/toxicology studies, with responsibilities including study design, execution and monitoring, vendor oversight, project management and cross-functional coordination. The position will report to the Director of Translational Biology. - Provide scientific leadership and oversight for in vivo pharmacology studies focused on inflammation, autoimmunity, or related therapeutic areas in support of advancing our pipeline programs. - As the pharmacology representative, collaborate cross-functionally with preclinical teams, project leads, and external consultants to advance our pipeline programs through in vivo studies. - Lead and monitor outsourced in vivo pharmacology and nonclinical safety/toxicity studies conducted at CROs, ensuring scientific rigor, protocol compliance, data quality, and timely execution. - Contribute to study design, protocol development and review, endpoint selection and data interpretation. - Review study reports, datasets and summaries to ensure scientific accuracy and completeness. - Coordinate study timelines, logistics, milestones, budgets and deliverables across multiple external vendors and internal stakeholders. - Manage CRO relationships, including vendor selection, performance oversight, issue resolution and communication. - Maintain study documentation, ensuring compliance with regulatory requirements and company policies. - Present study updates and recommendations to project teams and leads. - Support contract management activities (scopes of work, purchase orders, invoice tracking). Qualifications - Ph.D. degree in biology, immunology, pharmacology, toxicology, or a related field. - 3+ years of experience in biotech or pharmaceutical industry supporting nonclinical drug development programs. - Strong scientific background in inflammation, immunology, or immune-related diseases. - Extensive experience with rodent in vivo pharmacology and translational disease models. - Strong scientific data analysis, interpretation, and problem-solving skills. - Strong understanding of nonclinical drug development workflow. - Experience managing or monitoring outsourced studies at CROs. - Demonstrated project management and organizational skills with the ability to manage multiple studies and priorities simultaneously. - Excellent written, verbal, and presentation skills. - Ability to work effectively in cross-functional teams and to thrive in a highly collaborative research environment. Requirements - Experience with vendor oversight, contract management, and budget tracking. - Experience supporting IND-enabling and preclinical development programs. - Knowledge of GLP and non-GLP study conduct and non-clinical assessment. - Familiarity with IACUC regulations and animal welfare standards. Benefits - Compensation: $120,000 – $175,000 annually, depending on experience. - Participation in the Company’s stock option plan. - 3% retirement safe harbor contribution. - Fully paid health, dental, vision insurance for employees, spouses, partners, and families. - Above-market life insurance and disability coverage. - Additional benefits to explore during the offer process.
• Build AI-assisted workflows and internal tooling • Prototype and productionize LLM-enabled applications • Develop scalable analytics and decision-support systems • Improve self-service data access safely and responsibly • Reduce operational burden through automation and intelligent systems • Help evolve internal governance and semantic consistency practices
Founded in 1967, Capgemini is revered as one of the world's leading consulting, technology, and outsourcing agencies. In 2016 alone, the company reported global
Role Description Help support Auto and Home Actuarial team update Base Rate Offset tool into Python. This role requires strong Python skills as well as self-learning skills. - Contributes to the development and implementation of predictive analytics through the application of advanced statistical and analytical techniques in order to deliver data-driven insights supporting business objectives. - Uses appropriate modeling techniques to address business needs. - Utilizes broad knowledge of advanced modeling techniques and procedures to develop new modeling techniques and skills. - Conducts appropriate evaluation of model performance. - Designs and publishes reports to communicate results and track model performance. - Develops various programs including predictor and response variable programs. - Reviews programs to ensure they conform to quality standards. - Supports preparation of internal/external, structured/unstructured data sets to build/rebuild/refresh predictive models. - Creates ad-hoc data analyses, as needed. - Communicates analytics to other modelers as well as to non-technical business partners. - Contributes to the continuous improvement of the modeling process. Qualifications - English Proficiency: Fluent (We work 100% in English) Requirements - Python - Advanced - SQL - Intermediate - Power BI - Intermediate - Excel - Intermediate Benefits - Competitive salary and performance-based bonuses - Comprehensive benefits package - Career development and training opportunities - Flexible work arrangements (remote) - Dynamic and inclusive work culture within a globally renowned group - Private Health and Dental Insurance - Pension Plan - Meals tickets - Life Insurance
Chemistry is the key to drug discovery, but chemical data is stuck in the twentieth century. We’re generating precise chemical datasets purpose-built to propel drug discovery into the information age — and we’re doing it on a larger scale and faster than has ever before been possible. Terray Therapeutics is a biotechnology company led by pioneers and long-time leaders in artificial intelligence, synthetic (medicinal) chemistry, biology and preclinical development, automation, and nanotechnology. Chemical datasets generated using our novel ultra-dense microarray technology work seamlessly with our integrated machine learning and computational platform to systematically map biochemical interactions between small molecules and causes of disease. Iterative cycles of virtual molecular design and experimentation power AI and machine learning models, which in turn guide the next cycle of design. With a chemistry engine that measures billions of interactions daily and becomes increasingly precise with every cycle, we can answer an unprecedented array of questions — deriving insights that enable us to predictably create drugs for patients in need.
Role Description Terray is currently seeking a motivated and creative senior associate data scientist. As an integral member of our Computational and Data Sciences (CDS) team, the candidate will be responsible for analyzing and visualizing data from our computational discovery platforms. - Collaborate with project teams in applying platform tools to identify and prioritize hits for follow-up synthesis, testing, and pipeline progression. - Work closely with computational and assay teams to perform hit validation analyses to evaluate how tArray platform binding profiles translate to off-platform assays. - Partner with screening teams to develop and refine target-specific screening strategies. Qualifications - BS/MS in Cheminformatics, Computational Chemistry, data science, or a related scientific discipline with a strong interest in programming and data-driven research. - Understanding of chemical structures, molecular properties, and compound profiling in the context of high-throughput screening and hit identification. - Working knowledge of Python for scientific data analysis, automation, and workflow support, including experience with libraries such as pandas, numpy, scipy, and matplotlib. - Strong expertise in SQL, including building and querying custom tables for internal workflows. - Strong problem-solving and data analysis skills with the ability to communicate scientific insights effectively. - Experience working with large, real-world scientific or laboratory datasets. - Familiarity with Linux environments and version control tools (e.g., Git). Requirements - Exposure to machine learning methods such as clustering, regression, or pattern recognition is a plus. - Exposure to cheminformatics or scientific software tools (e.g., RDKit, Spotfire, MOE, Schrodinger) is a plus. Benefits - Compensation: $120,000 – $182,000 annually, depending on experience. - Participation in the Company’s stock option plan. - 3% retirement safe harbor contribution. - Fully paid health, dental, and vision insurance for employees, spouses, partners, and families. - Above-market life insurance and disability coverage. - Additional benefits to explore during the offer process.
• Design and implement scalable batch and real-time data processing systems across large and complex datasets. • Build and optimize ETL and streaming data pipelines using modern GCP big data technologies. • Lead development decisions around model choices, data architecture, data modeling, pipeline orchestration, analytics infrastructure, and production systems. • Develop statistical models and analytics capabilities that support product intelligence and operational insights. • Design and maintain production-grade data workflows using technologies such as Airflow, Dataflow, PubSub, and PySpark. • Contribute across multiple areas of the data ecosystem, including data engineering, monitoring and governance, visualization, and analytics tooling. • Establish monitoring, observability, and governance practices for data quality, pipeline reliability, and production health. • Partner closely with Engineering to operationalize scalable data infrastructure and analytics systems. • Collaborate with Product to shape intelligent, data-driven product capabilities and user experiences. • Act as a technical leader and thought partner across data engineering, analytics, infrastructure, and applied modeling initiatives. • Help evolve internal tooling and frameworks that improve scalability, reliability, and operational efficiency across the platform.
Driving Customer Success Through Finance Transformation: Advanced Processes, Analytics, & AI.
• Design, develop, and deploy machine learning and predictive analytics models. • Analyze large and complex datasets to identify trends, insights, and business opportunities. • Build scalable data science solutions and optimize model performance. • Collaborate with data engineering and business teams to define project requirements and deliver analytical solutions. • Develop data pipelines, feature engineering processes, and model evaluation frameworks. • Implement AI/ML algorithms for forecasting, classification, recommendation, and automation use cases. • Present analytical findings and recommendations to technical and non-technical stakeholders. • Ensure data quality, governance, and compliance standards are maintained. • Mentor junior data scientists and support best practices across the team. • Stay updated with emerging technologies and advancements in Data Science and AI.
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Python, SQL, Google Cloud Platform, AI/ML, Observability/Monitoring, Cloud
