Senior Data Scientist

Company Overview At Enlyte, we combine innovative technology, clinical expertise, and human compassion to help people recover after workplace injuries or auto accidents. We support their journey back to health and wellness through our industry-leading solutions and services. Whether you're supporting a Fortune 500 client or a local business, developing cutting-edge technology, or providing clinical services you'll work alongside dedicated professionals who share your commitment to excellence and make a meaningful impact.

Join us in fueling our mission to protect dreams and restore lives, while building your career in an environment that values collaboration, innovation, and personal growth. Be part of a team that makes a real difference. Job Description Key Responsibilities Design, develop, and implement machine learning models and algorithms to address business problems related to medical claims Conduct exploratory data analysis to identify patterns, trends, and insights Clean, preprocess, and transform large, complex datasets to ensure data quality Create compelling data visualizations and dashboards to communicate findings to technical and non-technical stakeholders Collaborate with engineering teams to deploy models into production environments Monitor model performance and implement improvements as needed Mentor junior data scientists and contribute to best practices documentation Stay current with emerging techniques and technologies in data science and machine learning Present findings and recommendations to leadership and business stakeholders Qualifications Bachelor's degree in Computer Science, Statistics, Mathematics, Data Science, or related quantitative field 3+ years of professional experience in data science, machine learning, or related analytical role Strong programming skills in Python or R, and proficiency with data manipulation libraries (pandas, numpy, dplyr) Experience building and deploying machine learning models using frameworks such as scikit-learn, Tensor.

Flow, or Py. Torch Solid understanding of statistical methods, experimental design, and hypothesis testing Proficiency with SQL and experience working with relational databases Experience with data visualization tools (Tableau, Power BI, or similar) Strong communication skills with the ability to translate technical concepts to non-technical audiences Preferred Qualifications Experience with cloud computing platforms (AWS, Azure, GCP) Experience with Snowflake cloud data platform, including: Writing optimized SQL queries for Snowflake's architecture Leveraging Snowflake's unique features like Time Travel, Zero-Copy Cloning, and Secure Data Sharing Working with Snowflake's semi-structured data capabilities (VARIANT, ARRAY, OBJECT types) Using Snowflake's integration with Python via connectors and Snowpark Experience with Snowflake's performance optimization techniques Knowledge of big data technologies (Spark, Hadoop) Experience with version control systems (Git) and collaborative development workflows Familiarity with containerization and orchestration tools (Docker, Kubernetes) Experience with NLP, computer vision, or time series analysis Contributions to open-source projects or research publications Required Technical Skills Programming: Python, SQL, R (preferred) Cloud Data Warehousing: Snowflake SQL, Snowflake data objects and architecture Machine Learning: Classification, regression, clustering, dimensionality reduction Data Processing: ETL pipelines, data cleaning, feature engineering Statistics: Hypothesis testing, experimental design, A/B testing Tools: Jupyter, Git, Docker (preferred) Visualization: Matplotlib, Seaborn, ggplot2, Tableau, PowerBI Soft Skills Problem-solving and analytical thinking Excellent written and verbal communication Collaboration and teamwork Attention to detail Time management and prioritization Continuous learning mindset Benefits We’re committed to supporting your ultimate well-being through our total compensation package offerings that support your health, wealth and self.

These offerings include Medical, Dental, Vision, Health Savings Accounts / Flexible Spending Accounts, Life and AD&D Insurance, 401(k), Tuition Reimbursement, and an array of resources that encourage a lifetime of healthier living.

Benefits

eligibility may differ depending on full-time or part-time status.

Compensation

depends on the applicable US geographic market. The expected base pay for this position ranges fr om $110,000 - $135,000 annually, and will be based on a number of additional factors including skills, experience, and education. The Company is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, religion, color, national origin, gender, gender identity, sexual orientation, age, status as a protected veteran, among other things, or status as a qualified individual with disability.

Don’t meet every single requirement? Studies have shown that women and underrepresented minorities are less likely to apply to jobs unless they meet every single qualification. We are dedicated to building a diverse, inclusive, and authentic workplace, so if you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyway.

You may be just the right candidate for this or other roles. #LI-AP1 #LI-Remote #ENT Key Responsibilities Design, develop, and implement machine learning models and algorithms to address business problems related to medical claims Conduct exploratory data analysis to identify patterns, trends, and insights Clean, preprocess, and transform large, complex datasets to ensure data quality Create compelling data visualizations and dashboards to communicate findings to technical and non-technical stakeholders Collaborate with engineering teams to deploy models into production environments Monitor model performance and implement improvements as needed Mentor junior data scientists and contribute to best practices documentation Stay current with emerging techniques and technologies in data science and machine learning Present findings and recommendations to leadership and business stakeholders Bachelor's degree in Computer Science, Statistics, Mathematics, Data Science, or related quantitative field 3+ years of professional experience in data science, machine learning, or related analytical role Strong programming skills in Python or R, and proficiency with data manipulation libraries (pandas, numpy, dplyr) Experience building and deploying machine learning models using frameworks such as scikit-learn, Tensor.

Flow, or Py. Torch Solid understanding of statistical methods, experimental design, and hypothesis testing Proficiency with SQL and experience working with relational databases Experience with data visualization tools (Tableau, Power BI, or similar) Strong communication skills with the ability to translate technical concepts to non-technical audiences Preferred Qualifications Experience with cloud computing platforms (AWS, Azure, GCP) Experience with Snowflake cloud data platform, including: Writing optimized SQL queries for Snowflake's architecture Leveraging Snowflake's unique features like Time Travel, Zero-Copy Cloning, and Secure Data Sharing Working with Snowflake's semi-structured data capabilities (VARIANT, ARRAY, OBJECT types) Using Snowflake's integration with Python via connectors and Snowpark Experience with Snowflake's performance optimization techniques Knowledge of big data technologies (Spark, Hadoop) Experience with version control systems (Git) and collaborative development workflows Familiarity with containerization and orchestration tools (Docker, Kubernetes) Experience with NLP, computer vision, or time series analysis Contributions to open-source projects or research publications Required Technical Skills Programming: Python, SQL, R (preferred) Cloud Data Warehousing: Snowflake SQL, Snowflake data objects and architecture Machine Learning: Classification, regression, clustering, dimensionality reduction Data Processing: ETL pipelines, data cleaning, feature engineering Statistics: Hypothesis testing, experimental design, A/B testing Tools: Jupyter, Git, Docker (preferred) Visualization: Matplotlib, Seaborn, ggplot2, Tableau, PowerBI Soft Skills Problem-solving and analytical thinking Excellent written and verbal communication Collaboration and teamwork Attention to detail Time management and prioritization Continuous learning mindset