Accenture people build careers in four different areas of our business: Consulting, Technology, Outsourcing and internal Corporate Functions. Each area offers a unique career experience and a compelling mix of work and training opportunities, work environment and structure for career progression.
Outsourcing professionals work on projects across a range of business processes, business applications and information technology. They may provide technology services, such as application management, infrastructure management and systems integration, or they may deliver business process outsourcing services, including finance and accounting, procurement and human resources. Depending on the role, outsourcing professionals may be based at a client’s office or in one of Accenture’s 50 delivery centers around the world.
The Lead Data Scientist will be part of Accenture Analytics as a Service Core Data Scientist team. They will be managing and coordinating with the team that identifies and develops advanced analytics statistical models, machine learning methods and solutions for Accenture operations clients to improve various business outcome indicators.
- Successfully develop, conceptualize and test various statistical and machine learning models
- Integrate the outcomes as real time analytics to elevate Accenture’s ability to create value for clients in areas and through means not immediately apparent to clients
Data Scientists in Operations follow multiple approaches for project execution from adapting existing assets to Operations use cases, exploring third-party and open source solutions for speed to execution and for specific use cases, and engaging in fundamental research to develop novel solutions. They also leverage the vast global network of Accenture to collaborate with Accenture Tech Labs, Accenture Open Innovation and Accenture Analytics for creating solutions. Data Scientists are expected to collaborate with other data scientists, subject matter experts, sales, and delivery teams from Accenture locations around the globe to deliver strategic advanced analytics projects from design to execution.
- Basic qualifications
- Masters or Ph.D. (Computer Science, Statistics, Engineering, Physics, Mathematics, Economics)
- Minimum 3 years' of Data Science experience for Ph.D. candidates and 5 years for Masters candidates
- Minimum 3 years' of work experience in relevant domains (F&A, Procurement, Fraud, Infrastructure Analytics, eCommerce, Security, Retail, Supply Chain Health Care, Pharma, Retail) – with hands on experience handling data driven decisions
- Minimum 3 years' of experience in at least one of the following – Supervised and Unsupervised Learning, Classification Models, Cluster Analysis, Neural Networks, Non-parametric Methods, Multivariate Statistics, Reliability Models, Markov Models, Stochastic models, Bayesian Models
- Minimum 3 years' of various statistical and machine learning models, data mining, unstructured data analytics in corporate or academic research environments
- Minimum 2 years' of experience in statistical and other tools/languages (SAS, C, C++, Java, Python)
- Minimum 2 years' of programming experience (C, C++, Java, SAS, Python)
- Experience leading or collaborating with a team of data scientists in developing and delivering machine learning models that work in a production setting
- Knowledge of UNIX or Linux environments
- Experience working with large data sets and tools like MapReduce, Hadoop, Hive, etc.
- Experience working with large data streaming technologies like Spark, Flink, etc.
- Familiarity with relational databases and intermediate level knowledge of SQL
- Ability to think creatively to solve real world business problems
- Ability to work in a global collaborative team environment
- Proficient verbal and written communication skills in English.