Accenture - Data Scientist

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:

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

Responsibilities :

- 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