Forensic Analytics is a fast-growth practice within Deloitte Risk and Financial Advisory that centers around several of the hottest areas of business today – analytics, forensic analysis, and litigation support – and the specialized skills that make careers in these areas both fascinating and in high-demand. Our Analytics team makes extensive use of data, statistical and quantitative analysis, rules-based methods, and explanatory and predictive modeling to bring insights to client issues in the forensic and transaction domains. We work with specialists in many areas, and we apply our solutions to a wide range of highly interesting and complex corporate challenges, such as forensic investigations, sales fraud, anti-corruption compliance, restructuring, safety and quality, enterprise fraud and misuse management to explain what has occurred in the past and to support informed decision making for the future. Our work increasingly employs specialized competencies, such as advanced analytics, visualization, and geospatial techniques
Work you’ll do
The Deloitte Managed Services & Products practice launched a new service in the forensics space to detect fraud, waste, and abuse in various industries. The Anti-Fraud Waste and Abuse solution ("AFES") continues to increase the capacity, quality, and efficiency of the Deloitte forensic processing by utilizing state-of-the-art technology and machine learning in a dedicated environment. We have an urgent need for a Lead Data Scientist/Senior Consultant to join the Deloitte Analytic & Forensic Technology practice. Here are just some to the things you will be doing:
- Translate client needs into robust end to end analytical solutions.
- Design and implement production data pipelines using distributed computing and cloud technologies
- Research and develop predictive models to identify and target fraud, waste, and abuse.
- Build features into our application to allow for actionable insights from predictive model outputs.
- Think creatively about how the latest advancements in machine learning can be implemented to help our clients uncover fraud, waste, and abuse.
- Review deliverables for accuracy and quality
- Provide coaching to junior staff
- Manage own personal and professional development; seek opportunities for professional growth and expansion of consulting skills and experience
The team
As a member of the AFES Data Science team in a Lead Data Scientist/Senior Consultant role, you will be a technical lead on a team that is committed to delivering advanced analytics and machine learning solutions to large healthcare clients. On a day to day basis, you will be helping research and develop cutting edge machine learning models and analytical solutions to identify fraud, waste, and abuse. As part of these projects, you’ll be provided the opportunity to demonstrate your technical, project management and leadership skills in an environment that provides for outstanding growth and advancement.
Qualifications
Minimum Qualifications:
- Bachelor’s Degree in Economics, Finance, Statistics, Mathematics, Computer Science, Management Information Systems, Engineering, Business Analytics disciplines, or related area
- 3+ years of experience working in a Data Science role.
- Proficient in Python, Spark(Pyspark), Kubeflow and SQL- Cloud exp
- Comfortable operating in a Linux environment.
- Familiarity with Big Data tools and frameworks such as Hadoop and Hive.
- Experience researching and developing machine learning models in a corporate setting
- Knowledgeable about use cases for supervised and unsupervised methods.
- Maintains a reasonable theoretical understanding of machine learning methods including, but not limited to, tree based methods(decision trees, random forest, gradient boosted trees), regression, clustering techniques, deep learning and neural networks.
- Experience optimizing and integrating models into production environments.
- Exposure to software design patterns (e.g. understand object oriented vs functional programming principals, inheritance, writing abstract, reusable, and modular code)
- Experience working in a cloud environment, preferably AWS.
- Familiarity with microservices oriented application development leveraging Docker
- Demonstrated problem solving and troubleshooting skills with experience exercising mature judgment
Preferred Qualifications
- Experience with at least one deep learning framework(e.g. Tensorflow, PyTorch, Caffe, MxNET)
- Experience working in an AGILE development team.
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