Deloitte Data Scientist Manager in Milwaukee, Wisconsin
Do you have a passion for artificial intelligence, machine learning, and data analysis? Do you yearn to have the impact of your work recognized and valued by more than just your development team? Do you constantly wonder what you could build if only you had access to world-class data sets and computing resources?
If yes, we have just the role for you.
In Deloitte's Audit and Assurance business, we make businesses and markets better. An audit is more than an obligation; it is an opportunity to see further and deeper into businesses. In our role as independent auditors, we enhance trust in the companies we audit, helping a multitrillion dollar capital markets system function with greater confidence. As we aspire to the very highest standards of audit quality, we deliver deeper insights that can help clients become more effective organizations.
Deloitte's Audit and Assurance business embraces the promise of artificial intelligence and machine learning, with various forms of AI and ML embedded in the audit technology solutions currently used by our 10,000+ practitioners today. Within our dedicated Data Science organization, a subgroup of our award-winning Audit Transformation group, we continue that journey enabling the next generation of AI-enabled solutions that usher in a future that completely transforms the way our practitioners perform their audit work, and the insights we can provide to our clients.
You will be joining a growing team of talented professionals in a fast-paced yet collaborative startup-like environment dedicated to realizing the Deloitte Audit and Assurance vision of an AI-enabled audit. You will be leveraging the most advanced technologies in machine learning, natural language processing, time-series modeling, and reinforcement learning to lead our business into the future.
As a Data Science Manager, you will lead the technical and technological components of our Data Science workstreams and AI and ML solutions. You will work hand-in-hand with subject matter experts to ensure the inputs and outputs suit the intended user experience and audit workflow. You will manage Data Scientists, Junior Data Scientists, and developers to complete the Data Science objectives that are required to fully develop and deploy a production-level ML solution. This includes performing or guiding research in an independent fashion, and interacting closely with key stakeholders with varying levels of machine learning experience.
Specifically, you will be expected to:
Have a profound understanding of the state of the art of a multitude of fields in artificial intelligence, including but not limited to NLP, probabilistic graphical models, time-series analysis, and weak supervised learning among others
Lead the application of rigorous data science within your workstream, managing and supervising junior resources to do so; particularly:
Perform or lead the performance of exploratory data analysis to understand relationships and opportunities to influence outcomes, while being able to quickly iterate common feature transformation and model types to find the best predictive models
Develop and document proofs of concept to verify your ideas, including counterfactual explanations for interpretability
Close the loop to make sure that the proposed solution is performing as it should and is correctly understood
Collaborate with subject matter experts to obtain a deep understanding of the underlying business problem, and to define and refine the corresponding technical solution
Co-lead the planning and direction of a project with subject matter experts, and effectively prioritize goals and objectives
Identify opportunities to apply the latest advancements in machine learning and artificial intelligence to build, test, and validate predictive models
Make impactful contributions to internal discussions on emerging machine learning methodologies
Influence machine learning strategy for current and prospective workstreams
Actively mentor Data Scientists and Junior Data Scientists on good software practices
Develop and embed automated processes for predictive model validation, deployment, and implementation
Architect ML pipelines and actively contribute high-quality, production-ready code (readable, well-tested, with well-designed APIs)
Undergraduate degree in a quantitative field (computer science, engineering, mathematics, physics, machine learning, statistics)
6+ years of industry experience leading the design, development, and deployment of machine learning models
Experience being the technical lead for multiple project teams simultaneously
Previous experience mentoring, training, and developing junior members of the team; experience in employee performance reviews
Expert understanding of Python and other common languages
Deep understanding of machine learning model development life cycles
Extensive experience using common machine learning and deep learning libraries and techniques, including TensorFlow, PyTorch, and big data platforms
Extensive experience with cloud-based ecosystems (Azure, GCP, AWS)
Experience in an Agile working environment and related project management tools (Jira, Azure DevOps, etc.)
Demonstrated ability to write high-quality, production-ready code (readable, well-tested, well-documented, with well-designed APIs)
Demonstrated ability to develop novel machine learning methods that go beyond putting together existing open-source code, and to apply problem-solving skills to complex issues
Experience with version control practices and tools (Git, etc.)
Fluency in both structured and unstructured data (SQL, NOSQL)
Solid understanding of Docker, Jenkins, Kubernetes, and other DevOps tools
Excellent written and verbal communication skills
Ability to travel 30%, on average, based on the work you do and the clients and industries/sectors you serve
Limited immigration sponsorship may be available
PhD in a quantitative field (computer science, engineering, mathematics, physics, machine learning, statistics)10+ years of industry experience leading the design, development, and deployment of machine learning models
Prior scientific publication history. Outstanding academic track record as evidenced by top tier publications.
Strong competency for additional coding languages (R, etc.)
Strong project management and delivery experience, including budget oversight and staffing of project teams including time management
Extensive experience with Microsoft Azure, including certification in machine learning
Experience with machine learning pipelines (Azure ML)
Experience with ML Ops and related governance processes, particularly within a regulated industry
Strong presentation skills using Microsoft Office suite (Visio, PowerPoint, etc.)
Understanding of the capital markets, and the role public accounting firms