Data scientist in advisory

KPMG Professional Services

Job Summary

KPMG's Data & Analytics professionals have earned that trust with a business-first approach focused on solving complex C-level business imperatives with analytics. We help clients address their long-term, strategic objectives. We combine our heritage of deep industry and process knowledge with innovative capabilities and proven solutions to help clients make better, faster decisions in all areas of their business to accelerate results.

  • Minimum Qualification: Degree
  • Experience Level: Management level
  • Experience Length: 5 years

Job Description

KPMG Professional Services and KPMG Advisory Services are the KPMG member firm in Nigeria. The partners and people have been operating in Nigeria since 1978, providing multidisciplinary professional services to both local and international organisations within the Nigerian business community. As one of the leading providers of professional services, KPMG knows that the success and growth of the firm also depends on the success and growth of the Nigerian economy. Hence, it champions progressive change and makes the future happen for its clients, people and the community, thereby enabling Nigeria’s success.

Responsibilities

  • Analyze and model structured data using advanced statistical methods and implement algorithms and software needed to perform analyses
  • Build recommendation engines, spam classifiers, sentiment analyzers and classifiers for unstructured and semi-structured data
  • Develop churn management, Customer Analytics, Predictive Analytics, Next Best Offer and other recommendation machine learning models and algorithms
  • Perform explanatory data analyses, generate and test working hypotheses, prepare and analyze historical data and identify patterns
  • Oversee the deployment of machine learning, natural language, and statistical analysis methods, such as classification, collaborative filtering, association rules, sentiment analysis, topic modeling, time-series analysis, regression, statistical inference, and validation methods
  • Design and implement cognitive computing/AI applications using some combination of the following commercial and open source platforms and libraries including Microsoft AI, Google AI, AWS AI, IBM Watson, Tensor flow, etc.
  • Participate in client engagements focused on big data and advanced business analytics, in diverse domains such as product development, marketing research, public policy, optimization, and risk management; communicate results and educate others through reports and presentations. Candidate should be open to working across industry groups including financial services, consumer markets, energy and natural resources, telecoms and public sector
  • Supervise and build capacity of junior members of the Data & Analytics team

Capabilities:

  • Strong knowledge in the following fields: predictive analytics and machine learning, natural language processing (NLP), Artificial intelligence (AI), data visualization, statistical modeling and data mining
  • Problem solving ability through the use and/or development of algorithms, models, testing, etc.
  • Strong understanding and ability to deploy supervised and unsupervised learning techniques including decision trees, ensemble methods, random forests, logistic regression, neural networks, SVM, Unsupervised learning & clustering, K-‘means, etc.
  • Strong knowledge and ability to leverage big data tools to cluster large amount of data and process data in distributed, large-scale environments. Familiarity with distributed data processing environments such as Amazon EC2, Storm, Hadoop and Spark will be an added advantage
  • Fluency in Python, R, Java, C++ or similar object oriented programming language
  • Proficiency and working experience with at least, one statistical modelling tool (such as SAS, Alteryx etc.)
  • Strong data cleaning and transformation skills to ensure available data is suitable for modelling
  • Strong communication skills - ability to explain technical concepts to the non-technical professionals/ client personnel

Experience and Qualifications

  • A minimum of five years of professional experience working as a Data Scientist in a practical problem-oriented business area
  • Strong experience in analytics, statistics, data mining, machine learning, natural language processing and/or mathematics
  • Master's degree or doctorate degree in Business Analytics, Computer Science, Statistics, Mathematics, Engineering or related fields
  • Minimum of second class upper in your first degree
  • Must be between 28 - 35 years old.

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