Health Data Scientist Jobs

**Our Company:**

Ilant Health is an obesity treatment company, focused on increasing access to obesity treatment while reducing total cost of care for employers and payers.We provide the single front door for individuals with obesity, delivering end-to-end evidence-based solutions (bariatric surgery, medication, intense behavioral therapy) through a technology-enabled and analytics-driven obesity medicine practice. Unlike our competitors who focus on diet and exercise and medication to the self-pay market, we focus on working with payors, employers, and providers to engage members and pair individuals with the right treatment and care coordination, enabling care through insured value-based care models.

**The Role:**

We are seeking a talented and experienced Health Data Scientist to join our team. As a Health Data Scientist, you will play a critical role in developing data models and analyzing and interpreting complex healthcare data to derive actionable insights and support evidence-based decision-making. The ideal candidate will have a background in health plan data analytics, with a focus on clinical data models in population health. This role requires strong analytical skills, proficiency in statistical modeling, and expertise in handling large-scale healthcare and claims datasets.

**Responsibilities:**

Lead development and refinement of data assets and models, incorporating learnings from existing research, claims data, and own data

Develop and implement statistical models and machine learning algorithms to uncover patterns, trends, and predictive insights from healthcare data

Analyze and interpret large-scale healthcare datasets, including claims data, electronic health records (EHR), and other clinical and administrative sources.

Own full data pipeline life cycle (i.e.., gather requirements, orchestrate jobs, write Python and SQL, implement data validation, track and problem solve in data pipelines, provide support).

Drive data model development (i.e. create, manipulate, and modify objects in data warehouse, write efficient transformations using data build tools, such as dbt, lead system design and architecture projects)

Investigate, evaluate, and develop new and existing tools to scale machine learning model deployment.

Work closely with stakeholders to define project objectives, data requirements, and deliverables.

Communicate findings and recommendations effectively to both technical and non-technical audiences through reports, presentations, and data visualizations.

Stay up-to-date with the latest advancements in health informatics, data science, and statistical methodologies, incorporating innovative approaches into research and analysis.

**Qualifications:**

Bachelor's or Master's degree in Computer Science, Actuarial Science, Applied Mathematics, Data Science, Statistics, or a related field.

2-3 years of professional experience at a health plan in data analytics, preferably with a focus on population health and risk-based models

Experience with population health management and value-based care/risk-based models

Strong proficiency in statistical analysis and modeling, with experience using tools such as R, Python (libraries such as Pytorch, Pandas, Numpy), or SAS.

Proficiency in SQL, Object-oriented programming language, data modeling (Star-schema or Snowflake), and databases.

Experience working with large-scale healthcare datasets, including claims data, EHR, and other clinical and administrative sources.

Knowledge of healthcare terminologies, coding systems (e.g., ICD-10, CPT), and healthcare regulatory requirements (e.g., HIPAA, HEDIS).

Experience developing predictive models (Linear Regression, Decision Trees, Random Forests, SVM, Neural Networks) and implementing machine learning techniques

Experience with data visualization tools and methodology (Tableau, Looker, etc).

Strong problem-solving skills and ability to work independently and collaboratively in a fast-paced environment.

Excellent communication skills, both written and verbal, with the ability to effectively present complex findings to technical and non-technical stakeholders.

**Preferred Qualifications:**

Knowledge of healthcare quality measures and performance metrics.

Familiarity with SQL and database querying.

Understanding of health plan operations and insurance concepts.

Experience in data warehousing (Snowflake, Google BigQuery, Databricks), pipeline orchestration, business intelligence, and ETL integration process.

Experience in building, productizing, and monitoring orchestration pipelines for AI and Machine Learning.

Experience using deep learning inference engines and optimization frameworks to deploy deep learning models (TritonRT, OpenVino, ONNX, or similar frameworks).

Experience with containers and orchestration (Docker and Kubernetes).