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Louisville, Kentucky-based Humana is a leading healthcare company that offers a variety of health, wellness, and insurance products and services designed to off
Title: Senior Data Scientist Location: Louisville, KY Boston, MA New York, NY Dallas, TX Tampa, FL Washington, DC (Arlington, VA) Full time Category:Technology and Digital Analytics Humana Job ID:R-421361 Job Description: Become a part of our caring community The Enterprise AI organization at Humana is a pioneering force, advancing AI innovation enterprise-wide across Humana’s Insurance, CenterWell, Growth, and Corporate business segments. By collaborating with world-leading experts, we are at the forefront of delivering cutting-edge AI technologies to improve care quality and experience of millions of consumers. We are actively seeking top talent to develop robust and reusable AI modules and pipelines, ensuring adherence to best practices in accountable AI. Join us in shaping the future of healthcare through AI excellence. We are seeking a Senior Data Scientist who will contribute to Generative AI initiatives at Humana, leveraging state-of-the-art LLMs to build applications. Our goal is to create safe AI solutions that will revolutionize and improve healthcare experience and outcomes for our customers. Join our rapidly expanding team of dedicated data scientists, engineers, policy experts, and business leaders as we work together to build impactful and beneficial AI systems. Key Responsibilities - AI Innovation & Responsible Generative AI Design and develop AI solutions using Python, AI/ML and generative models like LLMs. Ensure responsible development aligned with Humana’s ethical standards, focusing on transparency, safety, and real-world impact. - Agentic AI System Design Build autonomous AI agents using frameworks (i.e. LangChain, LangGraph, AutoGen, CrewAI) to support reasoning and decision-making in healthcare. - Enterprise Integration & Business Collaboration Embed AI into Humana’s systems and workflows. Partner with business teams to understand challenges, co-create solutions, and communicate AI capabilities clearly—especially around generative AI. Design for scalability, reliability, and compliance. - MLOps & DevOps Collaboration Work with engineering and product teams to implement best practices for deploying and maintaining AI models in production. - Execution of data science initiatives, including design, development, and implementation. - Collaborate with data scientists, date engineers, software engineers, and stakeholders to deliver high impact AI use case solutions. - Develop and maintain advanced AI/machine learning models and algorithms. - Design and implement robust monitoring capabilities to maintain optimal performance and reliability. - Experience in creating reports, projections, models, and presentations to business stakeholders. - Use your skills to make an impact Required Qualifications - Bachelor's Degree and 5 years of applicable experience OR Master's Degree and 3 or more years of experience preferably with a focus on developing production-ready AI solutions - Experience with Agentic AI System Design - Experience collaborating with MLOps and DevOps teams - Proficiency in SQL, Python, PySpark, and data analysis/data mining tools - Experience with machine learning frameworks like Scikit-Learn, Tensorflow, or Pytorch - Experience with high performance, large-scale ML systems - Experience with language modeling with transformers - Experience with large-scale ETL Preferred Qualifications - Ph.D. in Computer Science, Data Science, Machine Learning, or a related field. - Prior consulting experience Additional Information Work Style: This position will have a hybrid work style, with 3 days per week in office and 2 days per week remote/home. Office Location Options: - Louisville, KY - Boston, MA - New York, NY - Dallas, TX - Tampa, FL - Washington, DC (Arlington, VA) Scheduled Weekly Hours 40 Pay Range The compensation range below reflects a good faith estimate of starting base pay for full time (40 hours per week) employment at the time of posting. The pay range may be higher or lower based on geographic location and individual pay will vary based on demonstrated job related skills, knowledge, experience, education, certifications, etc. $117,600 - $161,700 per year This job is eligible for a bonus incentive plan. This incentive opportunity is based upon company and/or individual performance. Description of Benefits Humana, Inc. and its affiliated subsidiaries (collectively, “Humana”) offers competitive benefits that support whole-person well-being. Associate benefits are designed to encourage personal wellness and smart healthcare decisions for you and your family while also knowing your life extends outside of work. Among our benefits, Humana provides medical, dental and vision benefits, 401(k) retirement savings plan, time off (including paid time off, company and personal holidays, paid parental and caregiver leave), short-term and long-term disability, life insurance and many other opportunities.
• Build & Refine: Assist in building, testing, and refining predictive machine learning models and text-based Generative AI tools (utilizing Vertex AI and modern NLP frameworks) under the guidance of senior team members to address real-world clinical challenges. • Process & Analyze: Extract, clean, and preprocess structured healthcare data (EHR, billing, and claims), performing feature engineering to address noise, missingness, and complex clinical data structures. • Evaluate & Test: Conduct model evaluation, benchmarking, and error analysis, assessing performance metrics alongside clinical safety, fairness, and potential algorithmic bias. • Collaborate & Learn: Work alongside senior data scientists and clinical mentors to translate broad clinical questions into structured analytics problems and actionable technical tasks. • Document & Handoff: Write clean, well-documented code and maintain clear project documentation (in GitHub) to support model deployment and integration with MLOps pipelines.
A proactive problem-solver who can work independently and collaboratively Strong attention to detail and organizational skills Ability to translate business needs into technical solutions Passion for improving processes and user experience
Role Description Centurion is hiring an AI Senior Data Scientist to help our client in establishing an AI Lab to explore and implement generative AI and machine learning solutions that enhance staff productivity, improve analytical capabilities, and strengthen the Division's work in consumer protection and community development. We are looking for a full-stack Senior Data Scientist to support the AI Lab's research, development, and implementation of AI/ML solutions, with emphasis on generative AI applications. This role requires end-to-end ownership, from exploratory research and model development through application deployment and production maintenance. The ideal candidate is comfortable working across the full technology stack: building models, creating visualizations, developing applications, and deploying solutions to on-prem and/or cloud infrastructure. The AI Lab operates as a small, agile team where practitioners are expected to move between research, development, and deployment activities. This position will contribute to strategy while doing hands-on technical work, building models, training systems, evaluating performance, and deploying solutions. The AI Lab collaborates closely with DCCA's Data Analytics and Risk and Surveillance sections, and coordinates with the Board's enterprise technology on infrastructure, governance, and compliance matters. Responsibilities - Research, design, and develop machine learning and artificial intelligence solutions to support DCCA's mission, with emphasis on generative AI applications. - Build and iterate on proof-of-concept AI solutions that demonstrate value for specific use cases, transitioning successful prototypes into production applications. - Design and implement applications leveraging large language models for text analysis, summarization, information extraction, document classification, and workflow automation. - Develop prompt engineering strategies and retrieval-augmented generation (RAG) systems to improve AI application performance. - Experiment with fine-tuning, model customization, and evaluation techniques to optimize AI solutions for DCCA use cases. - Evaluate emerging AI technologies, frameworks, and models to identify opportunities for adoption within DCCA workflows. - Apply advanced statistical and machine learning techniques including supervised/unsupervised learning, classification, regression, and deep learning methods. - Build, deploy, and maintain AI/ML models and applications in cloud environments (AWS, Kubernetes, or internal analytics platforms). - Develop interactive dashboards and analytical applications using Python frameworks (Streamlit, Dash, Flask) or R Shiny. - Create data visualizations and user interfaces using Python libraries (Plotly, Matplotlib, Seaborn), R (ggplot2), Tableau, Power BI, or similar tools. - Manage deployment pipelines including containerization (Docker), CI/CD practices, and GenAI application deployments with API integrations, rate limits, and cost optimization. - Implement monitoring, logging, alerting, and visual dashboards for model performance, data quality, and system health. - Troubleshoot and maintain deployed applications, addressing performance issues, ensuring scalability, and updating applications as requirements evolve. - Support governance requirements including documentation for security assessments, privacy reviews, and compliance obligations related to deployed systems. - Work within a light agile framework, participating in sprint planning, standups, and retrospectives to coordinate work with team members. - Break down technical work into manageable tasks, estimate effort, track progress, and communicate status, blockers, and technical challenges to stakeholders. - Work directly with DCCA program staff economists, analysts, attorneys, and senior leadership to understand business needs, identify AI/ML opportunities, and translate requirements into technical solutions. - Communicate technical concepts effectively to both technical and non-technical audiences through presentations, reports, and executive summaries. - Document technical work, including code, methodologies, and project outcomes to support knowledge sharing and project continuity. - Contribute to building an AI/ML practice within DCCA, including documentation, capability development, and mentoring team members. - Work within federal IT governance frameworks including FISMA, privacy, and records management requirements as they apply to AI systems. - Coordinate with the Board's security, privacy, and compliance functions on matters related to AI Lab systems and applications. - Apply responsible AI practices including fairness evaluation, bias detection, model interpretability, and transparency in model development. - Maintain awareness of AI ethics, accountability, and appropriate use considerations in federal regulatory contexts. - Support preparation of documentation for system security plans, privacy impact assessments, and authority to operate processes when required. Qualifications - U.S. citizenship. - At least six years of hands-on experience developing, deploying, and maintaining AI/ML applications within a large, professional, or academic organization. - Bachelor's degree in Computer Science, Data Science, Statistics, Machine Learning, or related technology field (Master's degree preferred). - Expert proficiency in Python or R for data science development; experience with additional programming languages. - Production deployment experience: Demonstrated ability to build, deploy, and maintain AI/ML applications in cloud environments, including containerization and basic CI/CD practices. - Application development: Proficiency building interactive applications and dashboards using frameworks such as Streamlit, Dash, Flask, RShiny, or similar. - Data visualization: Strong experience creating visualizations and dashboards using Python/R libraries, Tableau, Power BI, or similar tools to communicate technical concepts to non-technical audiences. - AI/ML expertise: Advanced knowledge of machine learning, NLP (text normalization, Named Entity Recognition, POS tagging, word embeddings), and Generative AI technologies; experience with frameworks such as Scikit-learn, Spacy, XGBoost. - Statistical analysis: Advanced knowledge of statistical modeling, data analysis techniques, and problem-solving skills. - Ability to work independently and collaboratively, taking ownership of solutions from conception through production deployment. Preferred Qualifications - Prior experience in U.S. federal government, regulatory, supervisory, or policy environments. - Experience with financial services data, consumer finance, banking supervision, or regulatory data. - Experience working within agile frameworks (Scrum, Kanban) and project tracking tools (Jira, Azure DevOps). - Experience with LLM APIs (GPT, Llama, Nova) and frameworks (LangChain, LlamaIndex); knowledge of prompt engineering, fine-tuning, vector databases, and semantic search. - Familiarity with AWS AI services (Amazon Bedrock, SageMaker, Comprehend, Rekognition, Transcribe). - Experience building production-grade web applications with advanced user interfaces; knowledge of data storytelling and visual design principles. - Experience visualizing model performance metrics, feature importance, and model explainability outputs. - Hands-on experience with AWS deployment services (EC2, ECS, Lambda, S3, CloudWatch), Databricks, and infrastructure as code (Terraform, CloudFormation). - AWS certifications (Solutions Architect, Machine Learning Specialty, or similar). - Familiarity with MLOps practices including model monitoring, versioning, automated retraining, and deployment pipelines. - Experience with multi-modal AI applications; understanding of responsible AI practices (bias detection, fairness evaluation, model interpretability). - Familiarity with federal IT governance frameworks (FISMA, privacy requirements) and application security in regulated environments. - Experience working with sensitive or regulated data.
Rootshell Enterprise Technologies Inc. is a recognized provider of professional IT Consulting services in the US.
Role Description We are actively seeking a Data Scientist, Senior-Enterprise DS & AI Org for one of our clients. - Computer vision model experience a must. - Ability to synthesize complex information into clear insights and translate those insights into decisions and actions. - Demonstrated ability to explain in breadth and depth technical concepts including but not limited to: - Statistical inference - Machine learning algorithms - Software engineering - Model deployment pipelines - Competency in the mathematical and statistical fields that underpin data science. - Ability to develop, coach, and teach career-level data scientists in data science/artificial intelligence/machine learning techniques and technologies. - Strong in Python & R. Qualifications - Minimum: Bachelor's degree in Data Science, Machine Learning, Computer Science, Physics, Econometrics, Economics, Engineering, Mathematics, Applied Sciences, Statistics, or equivalent field. - Desired: Master's degree in one of the above areas. Requirements - Minimum: 4 years in data science (or 2 years if possess a master's degree). - Demonstrated knowledge of and abilities with data science standards and processes (model evaluation, optimization, feature engineering, etc.) along with best practices to implement them. - Competency in software engineering, statistics, and machine learning techniques as they apply to data science deployment. - Competency in commonly used data science and/or operations research programming languages, packages, and tools. - Hands-on and theoretical experience of data science/machine learning models and algorithms. - Mastery in systems thinking and structuring complex problems. - Desired: experience building computer vision models. - Desired: experience with AWS technologies (S3, GroundTruth, Sagemaker). Company Description Rootshell Enterprise Technologies Inc. is a recognized provider of professional IT Consulting services in the US.
• Serve as the senior analytics lead responsible for day-to-day coordination, issue tracking, communication with Government stakeholders, and alignment of contractor resources across workstreams. • Provide overall leadership, coordination, and accountability for contract execution across fraud analytics, emerging risk response, and portfolio reporting workstreams. • Support portfolio-level execution by tracking work performed, upcoming activities, staffing risks, dependencies, quality reviews, and required documentation in Government-designated systems. • Translate analytical findings, model/rule performance results, fraud metrics, release plan outputs, and operational indicators into clear recommendations for Government stakeholders and senior leaders.
• Translate business priorities into scalable data capabilities • Establish enterprise data standards • Lead cross-functional delivery across business and technology teams • Serve as the data engagement lead for an assigned business function • Co-develop and execute the enterprise MDM and Reference Data roadmaps • Lead MDM and Reference Data initiatives across critical domains • Establish governance processes, data standards, stewardship responsibilities, and quality controls • Lead the implementation and enhancement of Reltio MDM capabilities • Lead the implementation and enhancement of enterprise Reference Data capabilities • Advance semantic and terminology management capabilities • Partner with business teams, Data Governance, Architecture, Data Engineering, and other technology groups • Lead internal delivery teams and external partners
• Own the model lifecycle: requirements, experimentation, model development, evaluation, and model cards, partnering with ML engineers on deployment and production infrastructure • Translate complex fraud patterns into well-framed ML solutions: defining what to model, what success looks like, and where ML adds value vs. simpler approaches • Design and maintain feature engineering pipelines for model development • Monitor model quality in production, tracking performance over time, detecting data drift, and determining when to retrain • Partner closely with leadership, go-to-market, fraud operations, product, and engineering teams to define and execute effective fraud strategies • Champion a culture of continuous learning, experimentation, and collaboration across the fraud and broader data science teams
Role Description We are seeking a Healthcare Data Scientist (Real-World Evidence) to answer complex healthcare and biopharma questions using large-scale real-world data and help shape the next generation of AI-powered clinical research. This is a hands-on role that combines rigorous real-world evidence analysis with deep collaboration across product, AI, and customer teams. Internally, you will partner closely with Medeloop's AI research and product teams to: - Design and execute real-world evidence analyses - Evaluate the performance of our clinical research agents - Apply your scientific expertise to improve how our platform reasons about healthcare data Externally, you will serve as an embedded data scientist for our partner institutions, working directly with clinicians, researchers, and life sciences organizations to: - Scope research questions - Deliver high-quality evidence - Drive adoption of the platform - Expand each customer's use of Medeloop over time This is a highly technical role centered on analytical reasoning, statistical rigor, and scientific problem-solving. Your work will directly influence both the intelligence of our AI systems and the real-world impact they create for healthcare and life sciences organizations. Qualifications - PhD, or a Master’s degree minimum plus 5+ years of industry experience, in a quantitative-health field (biostatistics, epidemiology, clinical trials, public health, health informatics, or health economics). PhD preferred; strong industry experience can substitute for the doctorate. - Strong grounding in real-world data/evidence (RWD/RWE) methodology, with domain experience in biostatistics, epidemiology, clinical trials, or public health. - Proven ability to answer complex clinical or biopharma questions using SQL and Python, plus statistical software (R and/or SAS). - Experience with large healthcare datasets, such as claims or EHR data (clinical coding systems, ICD/CPT/RxNorm), assumed to come with an RWE background. - Track record of producing rigorous, research-grade analyses, reports, or publications; comfort working with messy, high-dimensional data. - AI/ML experience required, with clear evidence of hands-on use; comfortable reviewing code and reasoning about model outputs. - Strong communicator; customer-facing experience preferred (trainable for the right analytical candidate). Requirements - Experience working directly with customers and in a sales capacity. - Industry background strongly preferred over consulting (industry candidates preferred over consultants).
Role Description Your future role at a glance - Location: Remote - Department: Clinical and Population Health Analytics - Schedule: Full-time | Day shift - Salary: $96,208.99 - $134,109.89 per year How you'll make an impact in this role: - Build & Refine: Assist in building, testing, and refining predictive machine learning models and text-based Generative AI tools (utilizing Vertex AI and modern NLP frameworks) under the guidance of senior team members to address real-world clinical challenges. - Process & Analyze: Extract, clean, and preprocess structured healthcare data (EHR, billing, and claims), performing feature engineering to address noise, missingness, and complex clinical data structures. - Evaluate & Test: Conduct model evaluation, benchmarking, and error analysis, assessing performance metrics alongside clinical safety, fairness, and potential algorithmic bias. - Collaborate & Learn: Work alongside senior data scientists and clinical mentors to translate broad clinical questions into structured analytics problems and actionable technical tasks. - Document & Handoff: Write clean, well-documented code and maintain clear project documentation (in GitHub) to support model deployment and integration with MLOps pipelines. Qualifications - High School diploma equivalency with 2 years of cumulative experience OR Associate's degree/Bachelor's degree OR 4 years of applicable cumulative job specific experience required. Requirements - A Bachelor’s, Master’s, or PhD in Data Science, Computer Science, Health Informatics, Statistics, or a related STEM field. - Familiarity with or strong eagerness to learn healthcare data standards and clinical coding structures (e.g., ICD-10, CPT codes). - Solid foundational proficiency in Python (pandas, NumPy, scikit-learn), working knowledge of SQL (BigQuery/relational databases), and familiarity with version control tools like Git/GitHub. - Exposure to Large Language Model (LLM) concepts, prompt engineering, or modern NLP/GenAI frameworks (e.g., Hugging Face, LangChain, or Google Vertex AI) through coursework, research, internships, or projects. - Strong analytical problem-solving skills with the ability to communicate technical findings clearly to both technical peers and clinical stakeholders. Benefits - Paid time off (PTO) - Various health insurance options & wellness plans - Retirement benefits including employer match plans - Long-term & short-term disability - Employee assistance programs (EAP) - Parental leave & adoption assistance - Tuition reimbursement - Ways to give back to your community
• Develop new business relationships with end-user clients while creating a business case to utilize Carrier Commercial Service and Aftermarket for service within their facilities and for replacement projects. • Communication with end-user decision-makers including C-Level executives, VP of Operations, Directors of Facilities and Engineering professionals. • Responsible to maintain/expand these relationships within assigned, existing client accounts as well as build new customer relationships. • Act as a trusted advisor to customers. • Prepares and negotiates complex sales contracts with a large number of accounts. • Partner with the field sales and service team members, operations, branch and regional leadership as necessary. • Interacts well with Field Management and supports their business growth targets. • Work directly with building owners to improve system reliability, reduce energy consumption and improve the indoor environment. • Create and implement strategic sales strategies to successfully position Carrier as the preferred supplier to secure targeted projects in a competitive environment. • Manage current Carrier buying group relationships to maximize service and aftermarket sales. • Effectively perform needs assessments, develop/coordinate sales proposals and estimates, and presentations. • Work with operations, finance, legal and other inside and outside resources as needed to facilitate the sale and negotiate a satisfactory contract. • Prepare accurate and detailed sales activity reports, forecast reports, Sales Force updates and expense tracking. • Participate in professional organizations to build a network of contacts to advance achievement of sales targets. • Team sells with solutions partners to bundle solutions and expand Carrier participation in opportunities.
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Python, SQL, AI, AI/ML, R, LangChain
