Data Scientist II- Pulmonary

As a Data Scientist II, work closely with clinical leads and product sponsors to deliver an ML project from beginning to end, including designing, developing, testing the data solutions, and independently conducting silent and active pilot runs

• Collect, clean, and curate large and complex data sets from various sources to ensure it is suitable for machine learning tasks
• Lead the design, development, and optimization of machine learning applications to solve complex optimization problems
• Oversee data collection, preprocessing, and feature engineering to ensure high-quality datasets for model training
• Work with clinical leads and product owners to translate the optimization problem into product development design
• Design and run active pilot to measure usability, adoption, model performance and impact
• Stay updated on the latest advancements in ML and AI and implement novel algorithms and techniques when appropriate.
• Collaborate with the engineering team to deploy machine learning models into production environments, ensuring scalability and reliability.
• Mentor and guide junior members of the ML team, fostering a culture of learning and innovation.
• Collaborate cross-functionally with stakeholders to understand and address operational needs through machine learning solutions
• Research: Participate in research initiatives and contribute to publications and patents
• Effectively communicates statistical and technical ideas and results to non-technical stakeholders in written and verbal form to ensure decision points and their impact are clearly understood by all audiences
• Develop and maintain project work plans, including critical tasks, milestones, timelines, interdependencies, and contingencies.  Tracks and reports progress.  Keeps stakeholders apprised of project status and implications for completion. 
• Prepare clear, well-organized project-specific documentation, including, at a minimum, analytic methods used, key decision points and caveats, with sufficient detail to support comprehension and replication. 
• Mentor other analysts on how to a) determine appropriate statistical analysis methods, b) leverage appropriate programing languages and tools, and c) generate and interpret statistical analysis outputs.
• Share development and process knowledge with other analysts in order to assure redundancy and continuously builds a core of analytical strength within the organization.
• Adheres to corporate standards for performance metrics, data collection, data integrity, query design, and reporting format to ensure high quality, meaningful analytic output.
• Works closely with IT on the ongoing improvement of Mount Sinai’s integrated data warehouse, driven by strategic and business needs, and designed to ensure data and reporting consistency throughout the organization.
• Demonstrates advanced level proficiency with the principles and methodologies of process improvement. Applies these in the execution of responsibilities in support of a process focused approach.
• Other duties as assigned.

• Master's or Ph.D. degree in a quantitative discipline (e.g., Statistics, Operations Research, Bioinformatics, Economics, Computational Biology, Computer Science, Information Technology, Mathematics, Physics) or equivalent practical experience
• 4+ years of work experience in data science, software engineering, or data analysis
• 2+ years of hands-on experience with Scala and Python, including documentation for reproducibility
• 2+ years of hands-on experience with database languages (e.g., SQL, NoSQL)
• Strong knowledge of data preprocessing, feature engineering, and model evaluation techniques
• Applied experience in ML fields, such as natural language processing, computer vision, and statistical learning theory
• Experience handling terabyte-sized datasets, exploring data to discover hidden patterns, using data visualization tools, and leveraging GPUs for model development
• Proficiency in articulating technical concepts to diverse audiences, both technical and non-technical, and delivering data-driven presentations.
• Experience with cloud computing platforms (e.g., AWS, Azure, GCP)
• Experience with version control systems (e.g., Git)
• Knowledge of big data technologies (e.g., Hadoop, Spark)
• Self-motivated with a demonstrated ability to work independently, and to exercise independent judgment in developing complex techniques or programs in a dynamic environment.

• Publications or presentations in recognized journals or conferences
• Deep technical skills, 2+ years of experience in running active pilot and business savvy to interface with product owner and users working group
• Deep knowledge and hands-on experience in agile project management