KPMG is currently seeking a Lead Specialist to join our Federal Advisory practice.
Responsibilities:
* Lead Data Science and Machine Learning intensive engagements with a primary focus on client delivery and project management of teams with Data Scientists, Data Engineers, Business Consultants and translate advanced business analytics problems into technical approaches that yield actionable recommendations, in diverse domains (product development, marketing research, supply chain, technology process, and public policy)
* Undertake necessary statistical analysis using Natural Language Processing to mine unstructured data, using a variety of methods (document clustering, topic analysis, names entity recognition, document classification, and sentiment analysis) as part of the overall Machine Learning initiative set forth for the engagement
* Analyze structured and unstructured data, understand and implement algorithms, and support analysis using advanced statistical and mathematical methods form emerging areas in pattern analysis, machine learning, deep learning, data mining, econometrics, and operations research
* Lead workshops around emerging technologies in associated areas of Machine Learning, with both business clients and technical clients utilizing processes and best practices to plan, lead, and execute delivery of artificial intelligence engagements across different areas (technology, financial services, emerging tech, government agencies - federal, state, local, and utilities)
* Lead in a fast-paced and dynamic environment utilizing virtual and face-to-face interactions; manage complex work streams, expectations, budgets, deliverables, risks, and multiple responsibilities using structured approaches for operational excellence while communicating results to executive level audiences
* Plan and manage objectives and key deliverables using analytics processes to mitigate risks in data, modeling, validation, and delivery
Qualifications:
* A minimum of five years of technical data science experience; U.S. Federal government consulting experience preferred
* Bachelor's degree from an accredited college/university; Masters of PhD in Data Science, Engineering, Computer Science preferred
* Experience performing data science from data discovery, cleaning, model selection, validation, and deployment; experience coding artificial intelligence methods using object-oriented programming in a software development process, and ability to restructure, refactor, and optimize code for efficiency
* Expert problem-solving ability through the use and/or development of data science algorithms, models, testing, etc.; ability to utilize a diverse array of technologies and tools as needed, to deliver insights, such as R, Python, Spark, Hadoop and emerging Cloud Capabilities on Azure, GCP and/or AWS; experience with command-line scripting, data structures, and algorithms
* Ability to travel as required to support firm engagements
* Ability to obtain a U.S. Federal government security clearance within a reasonable period of time, which requires U.S. Citizenship
New York, New York
KPMG is a multinational professional services network, and one of the Big Four accounting organizations, along with Deloitte, Ernst & Young (EY), and PricewaterhouseCoopers (PwC). Seated in Amstelveen, the Netherlands, KPMG employs 207,050 people and has three lines of services: financial audit, tax, and advisory. Its tax and advisory services are further divided into various service groups.The name "KPMG" stands for "Klynveld Peat Marwick Goerdeler." It was chosen when KMG (Klynveld Main Goerdeler) merged with Peat Marwick in 1987.With a worldwide presence, KPMG continues to build on our member firms' successes thanks to our clear vision, maintained values, and our people.
At KPMG, our promise of professionalism to each other, our clients and the capital markets we serve compels us to align our culture of integrity with our values, words and actions. At KPMG we are committed to education and lifelong learning as they are central to building strong communities and economies.