Head of Enterprise Data Science

Functional Title

Head of Enterprise Data Science

Locations Preference

Buffalo NY, Baltimore MD, Wilmington DE, New York City NY

About the Team

Our team is on a mission of unleashing the power of data to support decision making. Our team builds enduring data products, provides platforms to access & derive insights from data, enables confidence in decision making with appropriate data governance, delivers actionable insights through use of data science and activates value for the business by innovatively solving problems with data. We love translating data, insights, and anecdotes into action, we operate with a sense of urgency, have a startup mentality, build data analytic products at scale, and innovate solutions on behalf of our customers. We work hard but value the need for downtime to unplug and recharge. We embrace our differences and view them as a key driver of innovation. We are Data@M&T

About the Role

As the Head of Enterprise Data Science, you will be a part of a team with a relentless focus on the craft of data science spanning statistics, machine learning, and distributed computing domains. Our relentless innovation is aimed at identifying business opportunities and delivering modeling solutions that enable growth and delivery of value to customers

Using domain understanding, you will translate unstructured, complex business problems into focused deliverables by identifying business requirements, developing modeling and analytical solutions across multiple problems, and optimally communicating insights, findings, and recommendations to business leaders

You will oversee the research and development of machine learning models through all phases of development; design, testing, data gathering, training, evaluation, validation, governance, and implementation. You will lead delivery of novel machine learning solutions including classification, regression, clustering, NLP, image analysis, graph theory and/or other techniques. You will identify, evaluate, and deploy new algorithms, data strategies, test plans, and implementation capabilities to drive continuous innovation

You will partner with cross-functional teams of product managers, engineers, stewards, and business analysts to launch machine learning solutions into production. You will actively identify and manage model risks in line with model risk management policies.

You build and leverage relationships across the organization to ensure proper understanding, adoption, utilization, and value realization of value from data science solutions in driving business outcomes. You will measure effectiveness and incrementality of utilization of machine learning solutions. You will create metrics and measure success of programs

You will have the opportunity to contribute to the culture and direction of the team. Your manager will support your career advancement and help you explore ways to develop. You'll have the opportunity to grow your scope of influence naturally as we scale and will have the opportunity to help hire and develop other leaders along the way

About you

You challenge conventional thinking and bring a depth and work with stakeholders to identify and improve the status quo.

You've built machine learning models over TB scale data, validated them, back tested them, and know what it takes to launch them in a real-time production environment.

You excel at partnering with cross functional teams to champion the field of data science to achieve collective goals.

You continually research and evaluate emerging technologies. You stay current on published state-of-the-art methods, and applications and seek out opportunities to apply them.

You make the most of the data available, while seeking new ways to proxy customer interest. You overcome setbacks and remain focused on delivering results.

Basic Qualification

• Bachelor's degree in quantitative/relevant field such as Statistics, Operational Research, Computer Science, Economics, Mathematics, Data Science
• Solid fundamental knowledge of supervised, unsupervised, reinforcement learning machine learning algorithms such as classifiers, cluster analysis, dimension reduction, regression, CNN, RNN, DQN, GAN, temporal difference methods, sequence modeling, NLP/NLU, collaborative filtering, self-attention, transformers, etc.
• Eloquent communicator able to translate data and complex machine learning concepts, drawing conclusions, defining recommended actions, and reporting results across stakeholders
• Strong business acumen with proven ability to solve business problems leveraging industry-leading data science/engineering methodologies with in-depth experience of manipulating and analyzing large high dimensionality unstructured datasets
• Experience in machine learning frameworks and tools (e.g. scikit-learn, mlr, caret, H2O, TensorFlow, MXNet, Pytorch, Caffe/Caffe2, CNTK, MLlib)
• Team oriented, collaborative, respectful, and flexible style, with the ability to tailor results to varied audiences
• Exhibit intellectual curiosity and strive to continually learn

Preferred Qualification

• Advanced degree in quantitative/relevant field such as Statistics, Operational Research, Computer Science, Economics, Mathematics, Data Science
• Experience with AWS, GCP, Azure, Hadoop, Containers, Dockers, and Git
• Knowledge of big data engineering stack including Hadoop, Spark, Kafka and other related components
• Experience with MLOps - scalable development to deployment of complex data science workflows
• Experience with model explainability and interpretability techniques
• Previous experience with a consulting firm delivery consulting services to financial services clients is a plus

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Location
Buffalo, New York, United States of America