Deloitte Senior Data Scientist in Gilbert, Arizona
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 Senior Data Scientist, you will focus on supporting the Data Science group and actively participate in all the processes of a data science project, with a focus on generating and selecting features, creating models, and boosting their performance and accuracy, as well as guiding and supervising more junior resources.
Specifically, you will be expected to:
Apply rigorous data science practices on your specific assigned workstreams; particularly:
Perform 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 proofs of concept to verify your ideas, including counterfactual explanations for interpretability
Collaborate daily with Data Science Managers to solve complex data science problems that emerge within your workstream and others.
Collaborate with subject matter experts to obtain an understanding of the underlying business problem, and to define and refine the corresponding technical solution.
Be a key contributor to the planning and direction of a project and effectively prioritize goals and objectives
Identify opportunities to apply the latest advancements in machine learning and artificial intelligence to build, test, and validate models
Make impactful contributions to internal discussions on emerging machine learning methodologies
Effectively explain technical concepts
Develop and embed automated processes for model validation, deployment, and implementation
Undergraduate degree in a quantitative field (computer science, engineering, mathematics, physics, machine learning, statistics)
3+ years of industry experience designing, developing, and deploying machine learning models
Experience with cloud-based ecosystems (Azure, GCP, AWS)
Extensive experience with Python and relevant libraries (NumPy, Pandas, Scikit-learn, etc.)
Solid understanding of machine learning model development life cycles
Demonstrated understanding of machine learning model performance assessment
Experience with version control practices and tools (Git, etc.)
Experience using common machine learning and deep learning libraries and techniques, including TensorFlow, PyTorch, and big data platforms
Fluency in both structured and unstructured data (SQL, NOSQL)
Knowledge of Docker, Jenkins, Kubernetes, and other DevOps tools
Excellent verbal and written communication skills
Ability to travel when necessary
Master's degree or PhD in a quantitative field (computer science, engineering, mathematics, physics, machine learning, statistics)
Extensive experience with Microsoft Azure, including certification in machine learning
Experience with machine learning pipelines (Azure ML)
Experience in an Agile working environment and related project management tools (Jira, Azure DevOps, etc.)
Public AI-related projects you have developed available on GitHub