About the Role

Hello Heart is seeking a talented and motivated Machine Learning Data Analyst to join our Users Data Science team. As a Machine Learning Data Analyst, you will be responsible for conducting in-depth analysis of our data science products, from research to production. Your work will directly contribute to improving healthcare practices and enhancing the well-being of individuals.

Your responsibilities

• Apply statistical analysis techniques, data mining algorithms, and visualization methods to extract meaningful patterns and trends from complex datasets.
• Monitor & analyze our models performance in production, generate insights and research leads for model improvements and new data driven solutions.
• Conduct AB tests, analyze the impact of models on users behavior, taking into account cause/effect, treatment/prevention relationships.
• Collaborate closely with cross-functional teams, including data scientists, data engineers, product managers, and healthcare professionals, to translate research findings into impactful digital solutions.
• Stay updated with the latest research papers, advancements, and best practices in the field of healthcare analytics.

Your Skills

• 2+ years of hands-on experience as a Machine Learning Data Analyst/Data Scientist. 
• Strong analytical and critical thinking skills, with the ability to interpret complex data and generate meaningful insights.
• Proficiency in statistical analysis tools (e.g., R, or Python with libraries like Pandas, NumPy, and SciPy) and data visualization techniques.
• Strong knowledge and experience with SQL.
• Familiarity with research methodologies, experimental design, and statistical modeling techniques (bayesian methods, propensity score matching, etc). 
• Experience in analyzing/testing the performance of ML models in production.
• Bachelor's degree in Statistics/Computer Science/Engineering or another related field.
• Experience in recommendation systems, user behavior segmentation, conducting AB tests for ML models in production, modeling the causal effects of treatment interventions - Advantage.