Senior ML Scientist with Healthcare OR Clinical data

Senior ML Scientist

Location: San Diego, CA(Remote)

Job Description:

The ideal candidate will have deep expertise in time-series data processing, as well as hands on experience with structured and unstructured data processing pipelines, and a solid background in statistics and machine learning/deep learning algorithms. In this role, you will develop, implement, and optimize machine learning and deep learning models tailored to complex multi-modal datasets. You will work closely with cross-functional teams, including AI leads, ML engineers, and product owners, to develop cutting-edge solutions that impact millions of lives worldwide. The Senior ML Scientist will directly report to the Head of AI.

Responsibilities:

• Design and develop machine learning models for time-series analysis, structured and unstructured data processing, and predictive analytics.
• Create and implement data preprocessing pipelines for various data types, including time-series, structured (e.g., tabular), and unstructured (e.g., text, images).
• Apply statistical techniques and feature engineering to optimize data representations for modeling.
• Train, fine-tune, and evaluate machine learning and deep learning models, ensuring high accuracy, robustness, and scalability.
• Stay up-to-date with the latest advancements in machine learning, deep learning, and time-series modeling, integrating innovative approaches into projects.
• Work closely with data scientists, software engineers, product managers, and clinical experts to align on project requirements and ensure successful model deployment.
• Prepare technical documentation, research papers, and presentations to communicate findings and results to both technical and non-technical audiences.

Qualifications:

• Ph.D. or Master's degree in Computer Science, Statistics, Data Science, or a related field with a focus on machine learning and data science.
• Minimum of 5 years of hands-on experience in machine learning, with a focus on time-series analysis, structured, and unstructured data processing.
• Proficiency in Python and libraries such as Pandas, NumPy, Scikit-Learn.
• Experience with deep learning frameworks such as TensorFlow or PyTorch.
• Strong understanding of time-series forecasting techniques, signal processing, and traditional statistical methods.
• Experience with handling unstructured (e.g., text, images) and structured data sources.
• Knowledge of feature engineering, model evaluation, and model interpretability.

• Strong analytical and problem-solving skills.
• Excellent communication skills, both written and verbal.
• Ability to work in a fast-paced, collaborative environment with cross-functional teams.
• Experience with healthcare or clinical data is a plus.