Lead Data Scientist

Job Description

Job Type: 1 year contract (w/ extension and possibility of permanent hire)

Location: 100% REMOTE (United States)

Must Haves

Masters or PHD in Data Sci, Comp Sci, Stat, or related field

5yrs+ Experience in data science roles, with a focus on the health ins. industry and/or in forecasting methods

Proficiency in Python and BigQuery, strong experience with Google Cloud

Expertise in ML-Ops, including working with DAGs and following CI/CD pipelines

Advanced knowledge of Time Series forecasting techniques, and ML Methods, including classics like SARIMA, exponential smoothing, time series regressions, and LSTM, XGBoost, etc

Understanding of and experience with causal inference methods (difference in differences, synthetic control, instrumental variables, etc) and experimental design

Problem-solving skills and the ability to work with complex data sets

Strong communication and the ability to lead/mentor a team of data scientists

Responsibilities:

Data Analysis: Lead the analysis of large, complex datasets to extract actionable insights.

Model Development: Develop and implement advanced machine learning models for forecasting and predictive analytics. As a key member of our team, you will be responsible for developing and standardizing forecasting applications using time series techniques to provide forward-looking insights to our clients, set performance guarantees, and drive’ improve Member experience

ML-Ops: Oversee the deployment and maintenance of machine learning models in production, ensuring robust and scalable solutions.

Collaboration: Work closely with cross-functional teams, including product managers, engineers, and business analysts, to understand business needs and deliver data-driven solutions.

Innovation: Stay up-to-date with the latest advancements in data science and machine learning, and apply them to improve existing processes and models.

Reporting: Create and present detailed reports and visualizations to communicate findings and recommendations to stakeholders.

Compliance: Ensure all data practices comply with industry regulations and company policies.

Full-time

Fully remote