About the Role
As a Data Scientist at Infosys, you will be responsible for the successful delivery of Advanced Analytics solutions and services in client consulting environments. You will work with various analytical tools and platforms, applying your knowledge of statistical techniques and deep learning to solve complex business problems.
Responsibilities
- Define key business problems to be solved and formulate mathematical approaches to address them.
- Gather data, develop, analyze, draw conclusions, and present findings to clients.
- Assist clients in identifying the type of analytics and management data that will directly improve market share or other client-specific measures.
- Identify differentiators, map them to the present-day environment, and design improvement plans.
- Discover new opportunities to optimize business through analytics and statistical modeling.
- Oversee all aspects of analytics and business intelligence projects, if necessary.
- Document or guide documentation of simple as-is customer processes.
- Appreciate the context of customer requirements from specific engagements.
- Suggest Analytics algorithms to use based on business situations, mindful of fundamental assumptions.
- Work with other data scientists as a team to understand algorithms suggested and provide QA in algorithm choice from a business perspective.
- Align deliverables to customer requirements and provide business acumen to avoid purely theoretical output.
- Converse with customers on the rationale for an approach taken, including pros and cons.
Requirements
Technical Skills
- Preferred proficiency with statistical analysis tools such as SAS, SPSS, R, and Python.
- Hands-on experience in Statistical Techniques & Deep Learning such as Decision Tree, Segmentation, Logistic Regression, and Multiple Regression.
- Experience in Forecasting, Campaign Management, and Customer Segmentation is desirable.
- Strong Excel, Access, and PowerPoint skills.
- Familiarity with database query tools (SQL).
- Basic understanding of data warehouse architecture.
- Understanding of various Analytical Tools, Platform & Frameworks.
- Experience in optimization (linear, mixed integer, constraint programming), simulation, and/or predictive analytics using AAO tools like SAS or SPSS, R and Python.
Industry & Functional Knowledge
- Knows basic principles of industry lexicon, competitor landscape, sub-verticals, and related best practices.
- Basic understanding of industry standards to document current customer compliance and gaps.
- Basic understanding of processes in relevant functional areas (e.g., Telecom – Fulfillment, Assurance, Workforce Management, Billing).
Analytical & Business Acumen
- Ability to look at data from multiple sources, integrate inputs, and build cause-effect linkages to arrive at key issues.
- Uses problem understanding to arrive at multiple solution alternatives, assessing pros and cons to identify optimal solutions.
- Understands the linkages of metrics and end objectives to key business drivers and aligns team efforts to deliver on them.
- Demonstrates basic understanding of business financial parameters and is aware of the business value impact of activities and decisions.
Communication
- Assesses target audience needs, prepares and practices a logical flow, answers questions appropriately, and adheres to timelines.