At: NASDAQ 100 Company
Location: East Coast
Position: Leader of Data Science
A highly successful and popular NASDAQ 100 company and a recent Forbes World’s Most Innovative Companies - is looking for a Leader of Data Science to lead our enterprise wide Data Science and Analytics division. We are seeking an accomplished technology professional with experience in digital transformation, business strategy, and AI engagement. This position will lead a team of 20 data scientists and act as an ambassador and chief data steward for the corporation. Many of the past and present projects coming out from this team have been recently featured on CNBC and Bloomberg TV. The position will have direct access to C-suite executives and will report to one level between the CFO of the company. We are looking for someone who is a quick learner, independent, curious, and passionate about data science and statistics.
The Leader of Data Science should be well versed in the creation, critique, and deployment of complex analysis, predictive, and prescriptive modeling that require the use of both structured and unstructured data, from multiple sources such as SQL, Oracle, Access Data Bases and text. Critical and technical review of the associated ETL and exploratory data analysis operations with implications of choices and assumptions. The position requires the ability to judge the business applicability of different analysis techniques such as Survival Analysis, ANOVA, proportions tests, linear, non-linear, and logistic regressions. Predictive and Prescriptive models use both supervised and unsupervised techniques for binary and multi-class predictions and clustering, that require different business dependent metrics such as AUC, lift, F1, accuracy, precision, recall, ROC. Familiar with the trade-offs associated with model accuracy and explanation (e.g. Explainable AI).
The Leader of Data Science will oversee the growth of cloud based services enabling the use of big data and advanced machine learning and AI algorithms. The development of an enterprise strategy for the advancement of Data Science that encompasses the use of tools such as R, Python, SAS, Hadoop, and Spark. The use of emerging auto-ML technologies to accelerate the development of new predicative models and AI-based systems. The enterprise adoption of tools and processes for the exploration, development, deployment, and maintenance of machine learning models and AI applications, based upon big data that are continuously changing.