Senior Data Scientist
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?
If yes, we have just the role for you.
In Deloittes 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.
You will be joining our collaborative Data Science team which leverages the most advanced technologies in machine learning and artificial intelligence to achieve the Deloitte Audit and Assurance vision of an AI-enabled audit. Some of our current projects focus on generative AI, prompt engineering, LLMs, anomaly detection, clustering, and knowledge graphs.
As a Senior Data Scientist, you will focus on supporting the Data Science group and actively participate in the entire lifecycle of a data science project, with a focus on feature engineering, 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 through the entire data science development and deployment lifecycle
Develop high-quality, production-ready code (readable, well-tested, with well-designed APIs)
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
Stay up to date with the latest trends, techniques, and advancements in data science and identify opportunities for their application
Effectively explain technical concepts
Qualifications
Required:
4+ years of relevant industry experience designing, developing, and deploying machine learning models
Undergraduate degree in a quantitative field (computer science, engineering, mathematics, physics, machine learning, statistics)
Solid understanding of machine learning model development life cycles
Deep knowledge in at least one of our core competencies (NLP and generative AI, Anomaly Detection, Time Series, Knowledge Graph)
Understanding of LLMs and prompt engineering
Demonstrated ability to write high-quality, production-ready code (readable, well-tested, well-documented, with well-designed APIs)
Extensive experience with Python and relevant libraries (NumPy, Pandas, Scikit-learn, etc.)
Experience with at least one cloud-based ecosystem (Azure, GCP, AWS)
Experience using common machine learning and deep learning frameworks such as TensorFlow, PyTorch, OpenAI and LangChain
Knowledge of Docker, Jenkins, Kubernetes, and other DevOps tools
Excellent verbal and written communication skills
Ability to travel as needed (<25%)
Preferred:
6+ years of relevant industry experience designing, developing, and deploying machine learning models
Masters 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.)
Proficiency in utilizing prompt engineering techniques to effectively guide the generation process and obtain desired outputs from language models.
Knowledge of finetuning techniques for language models, including methods to adapt and customize pretrained models for specific generative tasks, such as text generation, summarization, or dialogue systems.
Public AI-related projects you have developed available on GitHub