Lead Data Scientist

Description

Lead Data Scientist will work to build scalable, production ready machine learning and statistical models to improve healthcare data latency through automation. This role will focus on advanced statistical and machine learning solutions collecting, cleansing, interpreting large volumes of data from varying sources, designing and delivering production ready models, monitoring and maintaining models' health in production, all while communicating key findings with stakeholders. Serve as an expert on quantitative analysis, data mining, and presentation of data. Serve as expert on statistical and mathematical models for distinguishing relevant from irrelevant content or events.

Qualifications

Minimum Education

• Masters's degree in a quantitative/statistical or business field (e.g., Statistics, Mathematics, Engineering, Computer Science). (Required)
• Doctor of Philosophy (Ph.D.) preferred (Preferred)

Minimum Work Experience

• Requires deep functional knowledge with 10 years of related experience, or equivalent experience acquired through accomplishments of applicable knowledge, duties, scope and skill reflective of the level of this position (Required)

Required Skills/Knowledge

• Experience working in a heavily regulated industry. Healthcare is a plus.
• Advanced course in machine learning and programming.
• Experience working with global distributed multicultural teams.
• Experience with agile leadership.
• Experience with building, delivering and maintaining production ready machine learning models.
• Knowledge of statistical data analysis and machine learning such as linear models, time series forecasting, neural network, random forest and NLP models, etc.
• Expert in Python coding and utilization of machine learning and statistical packages for modeling.
• Experience with database skills, SQL, NoSQL, coding for ETL.
• In depth understanding of machine learning algorithms such as random forest, neural network, graph models, NLP, etc.
• Familiarity with Spark, Azure, Databricks, MLFlow AutoML.
• Experience and familiarity with backlog management tools and resources, ideally with JIRA and Confluence.
• Seeks to acquire knowledge in area of specialty.
• Ability to identify basic problems and procedural irregularities, collect data, establish facts, and draw valid conclusions.
• Ability to work independently.
• Demonstrated analytical skills.
• Demonstrated project management skills.
• Demonstrates a high level of accuracy, even under pressure.
• Demonstrates excellent judgment and decision-making skills.
• Ability to communicate and make recommendations to upper management.
• Ability to drive multiple projects to successful completion.
• Possesses technical aptitude.
• Excellent verbal and written communication skills, communicate complex findings in a clear and understandable manner.
• Excellent facilitation ability to host sessions and elicit ideas from others, understanding their issues and encourage group participation.
• Attention to detail.
• Collaborate effectively with cross-functional teams.
• Adapt to changing priorities and thrive in a dynamic environment.

Functional Accountabilities

• Develops and delivers statistical and/or machine learning models that solve business problems and work with engineers to make them production ready.
• Develops, utilize and monitor end-to-end machine learning pipeline from data ETL to model delivery for production.
• Leads rapid prototyping for new business problems to support feasibility analysis for AI products.
• Shares complex ideas verbally and visually with a broad audience from technical and non-technical backgrounds.
• Builds and adopts solutions to automate and integrate data science processes.
• Stays abreast of the latest and best solutions to solve data challenges at hand.
• Interprets and communicates results of complex models with cross functional team and the stakeholders.
• Generate internal implementations to achieve results.
• Work closely with the software engineering teams to drive scalable, production ready implementations.
• Collaborate with teams across the company and serve as an internal expert on technical issues.
• Document technical work and best practices as part of the production deployment process.
• Contribute to our evolving cloud infrastructure and data engineering pipeline.
• Contribute to scientific software engineering efforts utilizing professional coding standards.
• Engage with business partners to develop new models and concepts for continuous improvement.
• Performs other duties as assigned.
• Complies with all policies and standards.

Organizational Accountabilities

• Anticipate and responds to customer needs; follows up until needs are met.

Teamwork/Communication

• Demonstrate collaborative and respectful behavior.
• Partner with all team members to achieve goals.
• Receptive to others' ideas and opinions.

Performance Improvement/Problem-solving

• Contribute to a positive work environment.
• Demonstrate flexibility and willingness to change.
• Identify opportunities to improve clinical and administrative processes.
• Make appropriate decisions, using sound judgment.

Cost Management/Financial Responsibility

• Use resources efficiently.
• Search for less costly ways of doing things.

Safety