Lead Data Scientist

Title: Lead Data Scientist

Job Type: W2 Contract to Hire

Location: 100% REMOTE (United States)

Compensation: $75/hr max

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 casual 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.