• Analyzing and interpreting data sets to provide insights which will help make the performance marketing more efficient • Active collaboration with the UA team to adjust strategies based on data-driven insights • Build and maintain LTV forecasting models • Identify new signals (user behavior) for UA • Communicate complex data analysis and insights to non-technical stakeholders • Develop and maintain data pipelines to ensure accurate and timely data processing • Stay up-to-date with the latest technologies and advancements in Marketing Analytics

• uncovering strategic insights by combining deep product knowledge with rigorous data analysis • designing and analysing experiments to test hypotheses • defining, forecasting and influencing with metrics • supporting roadmap planning by surfacing insights

• 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.