• Apply machine learning, statistics, to develop algorithms • Solve challenging problems, signal processing, and computer networking domains • Leverage strong foundation in machine learning, data science, and signal processing to solve complex challenges in the RF domain. • Proficiency in designing and building data pipelines, including experience with ETL processes and data warehousing solutions • Hands-on experience with cloud-based infrastructure (e.g., AWS, Azure, GCP) for deploying ML solutions, including containerization, orchestration, and CI/CD pipelines for model deployment • Programming expertise in Python and SQL, with experience using data engineering frameworks (e.g., Spark, Airflow) and ML libraries (e.g., TensorFlow, PyTorch, scikit-learn) • Establish ML governance practices, including version control for datasets and models

• Collaborate with clinicians, researchers, and cross-functional teams to design and implement data science solutions that advance clinical research goals. • Conduct real-world evidence studies leveraging EHR data and unstructured clinical text mining. • Analyze large and complex clinical datasets using advanced statistical methods. • Develop predictive models and apply machine learning (ML) techniques to identify patterns, biomarkers, or risk factors in patient data. • Create visualizations and dashboards that effectively communicate complex findings to both technical and non-technical stakeholders. • Conduct data cleaning, preprocessing, and transformation to prepare datasets for analysis. • Apply Natural Language Processing (NLP) to extract insights from unstructured data such as clinical notes and patient narratives. • Work with large-scale healthcare data environments using big data frameworks and cloud computing platforms. • Ensure data integrity, security, and compliance with HIPAA and other relevant regulations. • Stay current with emerging technologies and methodologies in the data science and healthcare domains.

• Collaborate with stakeholders to identify business challenges that can be solved through data analysis. • Gather data from various sources (SQL databases, APIs, web scraping), then clean and "wrangle" it to ensure accuracy for modeling. • Design and implement algorithms and predictive models using machine learning techniques to forecast outcomes or categorize information. • Analyze datasets to uncover hidden patterns, trends, and anomalies. • Translate technical findings into "data stories" using tools like Tableau or Power BI to influence executive decisions.

• Operate as a bridge between the Data team and Risk team - not just to maintain what exists, but to explore what's possible. • Deeply understand Risk's problems, priorities, and workflows rather than delivering models over the fence • Build analytical and machine learning models that drive business decisions across our insurance/actuarial functions - including next-generation approaches that challenge industry conventions • Partner with our risk and insurance team to support loss modeling, pricing analyses, and portfolio performance metrics, while identifying novel data sources that haven't been leveraged in this industry • Design and run experiments (e.g. A/B tests, causal inference analyses) to measure product and pricing impacts • Translate complex analytical findings into clear recommendations for non-technical audiences • Dig into data, identify patterns, and move quickly from question to insight to action