Accenture Customer Analytics Role CMT Telecom Domain

  • 1-8 yrs
  • Not Disclosed

Job Description

Customer Analytics CMT

2 7 years of analytics overall experience, including at least 2 years of quantitative analysis in the CMT Telecom Industry

Exposure to Machine Learning with at least 2 years of practical experience in one or more approaches such as Random Forest, Neural Networks, Support Vector Machines, Gradient Boosting, Bayesian Networks, Deep Learning etc.

Hands on experience in Predictive analytics projects involving statistical modeling, customer segmentation etc.

Post Graduate degree in Statistics, Data Mining, Econometrics, Applied Mathematics, Computer Science or related field or MBA (Preferred)

Experience of working with US overseas markets is preferable

Set yourself apart

Key Competencies

Proficiency in two or more of analytical tools such as SAS product suite (Base Stats, E miner, SAS EGRC), SPSS, SQL, KXEN and any other statistical tools such as R, MATLAB etc. (SAS R are must)

Good knowledge of one of more programming language such as Python, Java, C++ is a plus

Advanced Excel including VBA and PowerPoint skills

Willingness to be flexible and work on traditional techniques as per business need

Consulting skills and project management experience is preferred

Excellent communication and interpersonal skills as well as collaborative, team player

Ability to tie analytic solutions to business industry value and outcomes

Autonomous, self starter with a passion for analytics and problem solving


Utilize state of the art Machine learning and optimization algorithms for targeting customers to increase profitability, acquire new customers and increase retention. Propose and apply new algorithms for the same

Demonstrated analytical expertise, including the ability to synthesize complex data, effectively manage complex analyses, technical understanding of system capabilities and constraints

Develop methodologies to support Customer Analytical project execution for CMT Telecom clients

Develop predictive analytics based solutions for

Customer Segmentation

Statistical Models across customer Lifecycle

Attrition Cross Sell Up sell Propensity Models

Customer Lifetime Value

Pricing Analytics

Web Analytics

Apply appropriate techniques, such as exploratory data analysis, regression, bootstrapping, trees, cluster analysis, survival analysis and so on

Develop and articulate strategic recommendations based on rigorous data analysis

Partner with client teams to understand business problems and marketing strategies