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Data Scientist

PamTen Inc

  • Minnetonka, MN
  • 8 days ago

    Highlights

    Responsibilities: Design, develop, and maintain anomaly detection and pattern recognition systems capable of identifying unusual behaviors, emerging trends, structural changes, and complex relationships across large-scale healthcare and operational datasets. Develop statistical and machine learning models using techniques such as clustering, classification, time-series analysis, change-point detection, outlier detection, graph-based analytics, ensemble methods, and representation-learning approaches.

    Numbers & Facts

    LocationMinnetonka, MN

    Description

    Required Qualifications
    • Bachelor's degree or equivalent experience in Data Science, Statistics, Computer Science, Engineering, Applied Mathematics, or a related quantitative field.
    • 5+ years of professional experience beyond degree in data science, machine learning, advanced analytics, statistical modeling, or a related technical discipline.
    • Experience developing machine learning or statistical solutions for complex, real-world business problems.
    • Experience with techniques applicable to anomaly detection, pattern recognition, classification, clustering, time-series analysis, or predictive modeling.
    • Proficiency in at least one programming language commonly used for data science and machine learning, such as Python, R, or SAS.
    • Strong SQL skills and experience working with large relational or analytical data platforms.
    • Experience using source control and collaborative software development practices.
    • Experience developing reusable, maintainable analytical code rather than exclusively notebook-based or ad hoc analyses.
    • Ability to communicate technical concepts, analytical findings, system behavior, and model limitations to both technical and non-technical stakeholders.

    Preferred Qualifications:
    • Master's degree in a quantitative, computational, or engineering discipline.
    • Experience working with healthcare data, including claims, clinical, member, provider, financial, or operational datasets.
    • Hands-on experience developing anomaly detection or pattern recognition systems using supervised, semi-supervised, or unsupervised learning techniques.
    • Experience with advanced modeling approaches such as ensemble methods, deep learning, graph analytics, natural language processing, embeddings, or large language models.
    • Experience with CI/CD platforms and automated deployment workflows for analytical or machine learning applications.
    • Familiarity with MLOps practices including model registries, experiment tracking, automated testing, model versioning, deployment strategies, monitoring, observability, and model lifecycle management.
    • Experience with workflow and pipeline orchestration technologies used to automate data processing, model training, scoring, and deployment.
    • Experience integrating machine learning or analytical services with other technology systems through APIs, services, event-driven processes, databases, or enterprise applications.
    • Experience working in cloud-based analytics environments, particularly Azure and Snowflake.
    • Familiarity with containerization, infrastructure automation, or modern software engineering practices used to deploy and operate analytical workloads.
    • Experience troubleshooting complex analytical systems across data, model, pipeline, infrastructure, and application layers.
    • Ability to balance statistical rigor, technical scalability, explainability, maintainability, and business usability when designing analytical solutions.

    Responsibilities:
    • Design, develop, and maintain anomaly detection and pattern recognition systems capable of identifying unusual behaviors, emerging trends, structural changes, and complex relationships across large-scale healthcare and operational datasets.
    • Develop statistical and machine learning models using techniques such as clustering, classification, time-series analysis, change-point detection, outlier detection, graph-based analytics, ensemble methods, and representation-learning approaches.
    • Build analytical solutions that combine multiple data sources and signals to recognize patterns that may not be detectable through traditional rules-based or single-variable approaches.
    • Evaluate model performance using appropriate statistical techniques and develop methods for threshold optimization, signal prioritization, false-positive reduction, model calibration, and explainability.
    • Develop reusable feature engineering, scoring, and analytical components that can support multiple enterprise use cases rather than isolated point solutions.
    • Apply natural language processing, large language models, and other machine learning techniques to unstructured and semi-structured information to identify patterns, themes, relationships, and emerging signals.
    • Design and contribute to production-grade machine learning and analytical pipelines, including automated data preparation, feature generation, model training, validation, deployment, scoring, and monitoring.
    • Develop and maintain CI/CD workflows for data science solutions, incorporating source control, automated testing, environment management, deployment automation, model versioning, release controls, and rollback capabilities.
    • Partner with engineering and technology teams to integrate analytical models and services with enterprise applications, data platforms, APIs, workflow systems, and downstream business processes.
    • Establish monitoring for production analytical systems, including model performance, data quality, feature drift, model drift, pipeline health, processing failures, and other operational indicators.
    • Investigate production issues and analytical anomalies through systematic root-cause analysis, working across data, modeling, infrastructure, and application layers as needed.
    • Contribute to architecture and technical design decisions related to scalable analytics, model serving, orchestration, integration patterns, and production machine learning.
    • Support analytics infrastructure and tooling, including technologies such as Snowflake and Azure, to ensure solutions are scalable, reproducible, observable, secure, and aligned with enterprise technology and data governance standards.
    • Collaborate with business, analytics, engineering, architecture, and technology stakeholders to translate complex analytical requirements into reliable technical solutions and measurable business outcomes.
    • Research and evaluate emerging statistical, machine learning, AI, and MLOps techniques and determine their applicability to enterprise analytical problems.

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