Accenture - Data Scientist (3-6 yrs)
YOUR EXPERIENCE : Basic Qualifications (subhead)
- 3-6 years of analytics overall experience.
- 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:
- 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 :
i. Customer Segmentation
ii. Statistical Models across customer Lifecycle
iii. Attrition / Cross-Sell / Upsell Propensity Models
iv. Customer Lifetime Value
v. Pricing Analytics
vi. 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
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