Manager Data Science

Primary Responsibilities

• Apply advanced machine learning and predictive modeling techniques to build, maintain, and improve on multiple predictive detection engines in Optum Enterprise Analytics.
• Develop actionable machine learning solutions and propose recommendations and strategies to solve commercial opportunities in unique ways that return optimal value to our business.
• Design and implement innovative analytic solutions including anomaly detection, member profiling and population level prediction and exploratory data analysis from unstructured and diverse datasets.
• Apply knowledge of statistics, machine learning, programming, data modeling, simulation, and advanced mathematics to recognize patterns, identify opportunities, pose business questions, and make valuable discoveries to prototype development and product improvement.
• Use a flexible, analytical approach to design, develop, and evaluate predictive and prescriptive models and advanced algorithms that result in optimal value extraction from the data.
• Work with analytics and statistical software such as SQL, R, Python and Big Data tools to perform analysis and interpret data.
• Deliver robust and scalable analytic solutions in an automation ready state.
• Actively consult with business stakeholders and subject matter experts to ensure appropriate medical and business content is incorporated into analytic approaches.
• Collaborate with lines of business to understand their analytic needs, contribute to shaping business solutions and communicate results of analytics.
• Develop and expand a fledgling applied research program with focus on impact-focused initiatives, rapid prototyping, and obsession with converting applied research into software.
• Be a hands-on leader and teach by example by building prototypes using variety of predictive methods while adhering to the ML development cycle (e.g. iterative EDA, prediction specification, feature engineering and model tuning).
• Participate in meetings business stakeholders, data experts, software/platform engineering, physicians to scope R&D use-cases and convert them into scope of work.
• Be a thought leader and coach the junior team members in the wide variety of statistical/predictive methods (e.g. DL, VAE, Bayesian nets etc) to attack a given prediction use-case.
• Self-drive knowledge accumulation of methods and modeling advances by reading research publications, blogs and attending presentations and webinars.
• Build, mentor and lead a team of applied research scientists and data scientists.

Required Qualifications

• Graduate (Masters/Phd) degree in engineering, information, or statistics.
• 7+ years of experience conducting statistical, research-minded analysis of variety of data types/forms with aim to utilize this analysis into building or optimizing predictive methods.
• 5+ years of experience in programming with languages such as Python, R, SAS.
• 4+ years of deep, hands-on experience with machine Learning applied to the healthcare or other high-stakes domains (direct experience in a cloud environment is highly valued).
• Experience applying computational algorithms, statistical and programming methods using (R and Python) to structured and unstructured data.
• Proven experience in understanding complex problems and datasets and driving data testing and strategic testing of ideas to come up with first-class solutions.
• Demonstrable success within a Data Science environment focused on agile thinking and innovative solutions, delivering complex and large-scale projects.
• Demonstrated ability to own and lead projects that deliver real business value.

Preferred Qualifications

• MS/PhD in applied mathematics, computer science, engineering, statistics, bioinformatics, actuarial science, or similar numerical degree.
• Experience with one or many of these topics such in customer churn, intent prediction, disease/damage progression, anomaly detection, sequence-based models, time-series forecasting or time to event modeling.
• Experience in deploying machine learning models in a production setting (logging, monitoring, alerts) in a cloud environment.
• Experience and understanding of US healthcare system, billing of medical claims and associated data environments.
• Experience creating Machine learning solutions to solve complex issues.
• Exposure to extracting and manipulating data via tool/libraries/forms such as Spark, pandas, scikit family, numpy/scipy, matplotlib, streamlit.