Data Scientist

Full time

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Employment Information

Job levelMedium level

IndustryHospitals and Health Care

Experience3+ years

LocationFL, United States

Overview

Lucina Analytics, a partner of Unified Women’s Healthcare, is a data-driven, enterprise SaaS company that delivers the leading women’s maternity analytics platform to innovative health plans, providers, and public entities.

The Data Scientist will join the data analytics team and collaborate in building models or developing methodologies to support Lucina’s maternal health solutions. The ideal candidate will have experience in using, manipulating, and extracting insights from healthcare data with a particular focus on using machine learning with claims data. The applicant will be driven by curiosity, collaborating with a cross-functional team of Product Managers, Software Engineers, and Data Analysts.

Responsibilities

Apply data science, machine learning, and healthcare domain expertise to advance and oversee Lucina’s pregnancy identification and risk-scoring algorithms.
Analyze healthcare data to study patterns of care and patient conditions which correlate to specific outcomes.
Collaborate on clinical committee research and development work.
Complete ad hoc analyses and reports from internal or external customers prioritized by management throughout the year.

Qualifications

Degree or practical experience in Applied Math, Statistics, Engineering, Information Management with 3 or more years of data analytics experience, Masters degree a plus.
Experience manipulating and analyzing healthcare data (payer’s claims experience a plus)
Strong communication skills with ability to describe complex ideas to key stakeholders
A proven track record applying Data Science to healthcare data. Technical skills include, but are not limited to, regression models, classification techniques, decision trees, clustering, pattern recognition, supervised and unsupervised learning, the bias-variance tradeoff, and neural networks
Experience with clinical design patterns and causal modeling techniques (e.g., GLM methods, propensity score matching, etc.) a plus
Proficiency in Python and core data science libraries such as Pandas, SciKit-Learn, NumPy, Matplotlib, SciPy, and Keras.
Advanced SQL and experience working with relational databases and data warehouses
Experience with two or more of the following: SQL Server, SQL Data Warehouse Appliance, Netezza, Hadoop, Spark, Snowflake, Tableau, Power BI, or similar data technologies.
AWS, Azure, GCP or similar cloud experience a plus
Familiarity with CI/CD and Git (commits, pushes, branching, and pull requests) a plus
Experience with Databricks or PySpark a plus