Healthcare Data Scientist

• Work with stakeholders throughout the organization to identify opportunities for leveraging company data to drive business solutions.
• Collaborate with internal data engineers and data analysts to collect and preprocess data, and build and maintain data pipelines
• Cross collaboration with internal and external stakeholders for the development and validation of data sets for clinical research and population level health initiatives.
• Mine and analyze data from company databases to drive optimization and improvement of product development, marketing techniques and business strategies
• Assess the effectiveness and accuracy of new data sources and data gathering techniques.
• Develop custom data models and algorithms to apply to data sets.
• Use predictive modeling to increase and optimize clinical outcomes, customer experiences, revenue generation, ad targeting and other business outcomes.
• Develop company A/B testing framework and test model quality.
• Coordinate with different functional teams to implement models and monitor outcomes.
• Develop processes and tools to monitor and analyze model performance and data accuracy.
• Data Management: Utilize big data processing software to collect, collate, and organize healthcare data efficiently.
• Analysis: Identify patterns, trends, and insights within the data, enabling informed decision-making.
• Information Dissemination:
  • Share relevant information on a small scale (e.g., transferring patient data between doctors) and a grand scale (e.g., sharing insights about new illnesses globally).
  • Use data visualization tools to communicate insights and findings to stakeholders
  • Collaborate with various stakeholders to ensure effective communication.
• Industry Optimization: Recommend ways to improve healthcare industry efficiency and performance based on data-driven insights.

• Strong understanding of probability and statistics.
• Knowledge of how healthcare data fits into different aspects of the industry.
• Strong analytical and problem-solving skills with an emphasis on product development.
• Experience using statistical computer languages (R, Python, SLQ, etc.) to manipulate data and draw insights from large data sets.
• Experience working with and creating data architectures.
• Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
• Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests, and proper usage, etc.) and experience with applications.
• Knowledge of data visualization tools (Tableau, Power BI, or matplotlib)
• Excellent written and verbal communication skills for collaboration with cross-functional teams.
• A drive to learn and master innovative technologies and techniques.
• Remote Work Skills: Proven ability to work with remote teams and independently.
• Communication: Excellent verbal and written communication skills.
• Problem-Solving: Strong analytical and problem-solving abilities.
• Adaptability: Ability to thrive in a dynamic and fast-paced environment.
• Proficient communicator: Fostering seamless internal and external relations across all organizational levels.

• Minimum 5-7 years of experience manipulating data sets and building statistical models
• Bachelor’s or master’s degree in data science, computer science, mathematics, statistics, or related quantitative field and relevant experience. Master’s degree or PhD preferred.
• Familiarity with the following software/tools:
  • Coding knowledge and experience with several languages: C, C++, Java, JavaScript, etc.
  • Knowledge and experience in statistical and data mining techniques: GLM/Regression, Random Forest, Boosting, Trees, text mining, social network analysis, etc.
  • Experience querying databases and using statistical computer languages: R, Python, SLQ, etc.
  • Experience using web services: Redshift, S3, Spark, Digital Ocean, etc.
  • Experience creating and using advanced machine learning algorithms and statistics: regression, simulation, scenario analysis, modeling, clustering, decision trees, neural networks, etc.
  • Experience analyzing data from 3rd party providers: Google Analytics, Site Catalyst, Coremetrics, Adwords, Crimson Hexagon, etc.
  • Experience with distributed data/computing tools: Amazon Quick Site (AQS), Map/Reduce, Hadoop, Hive, Spark, Gurobi, MySQL, etc.
  • Experienced presentation skills, specifically visualizing/presenting data for stakeholders using: Periscope, Business Objects, D3, ggplot, etc.

About PaceMate
Recognized as a Top 100 Healthcare Technology Company by Healthcare Technology Report, PaceMate offers the most comprehensive remote cardiac monitoring solution in the industry. PaceMateLIVE is the only clinical dashboard with user-friendly integrations for all implantable cardiac devices, ambulatory monitors, consumer ECGs, and heart failure products. PaceMate’s Auto-Triage™ prioritizes patients based on clinician-customized standards—not device alerts—resulting in a substantial reduction in alert burden. With the most live connections through EHR-partnered integrations across Epic, Cerner, and athenahealth, PaceMate offers a true, vetted, and validated industry-leading solution for interoperability: ensuring bi-directional data transfer for more accurate reporting, streamlined workflows, and one-click billing. PaceMate’s software-only, concierge on-demand, and full-service offerings deliver a flexible, customized solution for cardiac practices. Visit PaceMate.com to learn how one platform is transforming the future of digital health.