• Post-grad in one of the following fields with strong academic credentials:
• Computer Science/IT.
• Operations Research/Applied Math.
• Engineering.
• Statistics.
Responsibility:
Business:
'•Works with the business team to identify the right business problem, gather the requirements and data required to answer the same.
•Data exploration , hypothesis testing and statistical modeling are part of daily activities.
•Involved in development , testing, evaluation and optimisation of models developed.
•Analyzes data and generates insights that can articulated to business stakeholders.
•Develops hypothesis for testing in consultation with Principal/Domain SME and Business teams.
Stakeholder Management:
'• POC for all the daily based activities and ensures the availabilty of all the required information with all the team at all the times.
'•Build the collaterals which are durable and reusable •Communicate analytical results in a way that is meaningful for business stakeholders and provides actionable insights.
• Coordinates in communicating the data needs with both technology and business teams to ensure that right data is captured for analysis and modeling.
'Design qualitative & quantitative research instruments & methods (example: machine learning models, surveys, interviews etc) to capture the data if required.
Integrate qualitative & quantitative information to create insights.
Project Management:
'•Ensures that all the deliverables meets the delivery excellence standards and meets the stakeholders' expectations.
• Identifies risks to project execution and works with stakeholders to mitigate the same.
• Execute the design, analysis, or evaluation of assigned projects using sound engineering principles and adhering to business standards, practices, procedures, and product / program requirements.
Data Analytics and Reporting:
'• Explore and examine data from multiple disparate sources.
Prepare a data collection plan from both structured and unstructured sources.
Collaborate and coordinate with Technology and Business teams for all data needs.
• Expert level proficiency in data handling (SQL).
Data Discovery & Profiling:
'• Perform exploratory data analysis and generate insights.
Validate hypothesis developed during exploration phase.
Present initial results to business stakeholders and identify the next steps.
Design experiments with test and validate multiple hypothesis to meet/exceed expectations of customer due to the dynamic environment.
Data Modelling:
Create models using one or more of the platforms like R, SAS, Python, Matlab Model creation would involve one or more of the following technqiues:
1 Classification.
2 Clusterning, Segmentations.
3 Time Series.
4 Market Basket Anaysis.
5 Text Mining(Structured and Unstructured Data).
6 NLP, NLU, NLC.
7 Decision Trees, RF.
8 Network Analysis.
9 Linear Programming.
10 Optimisation.
11 Deep Learning.
'• Testing and validating the model.
• Deriving insights and recommendations from the models.
• Performing data visualization and presentation to clients.
Innovation & Thought Leadership:
'• Provide thoughtleadership and dependable execution on diverse projects.
• implement best practices and technology.
• Discover new avenues by disecting the data and identify which all models can be utilised for a given business problem.
• Provide expertise thru PoCs and PoVs.
Knowledge Management:
'Prepare a design, requirement document.
Document all modeling steps in a systematic way including modeling process, insights generated , presentations , model validation results and checklists built in the project.
Prepare a one pager document that outlines and quantifies the business impact due to the DS project.
People/Team Management:
' Mentor a team of Data Scientists.
Set the timelines and monitor the progress of the project.
Ensure the timely delivery of deliverables and addresses the concerns related to tasks.
Understand aspirations of team members.
Set goals for team members and monitor performance.
Conduct appraisals.
Identify, priorities and deploy action items for competency development.
Guide the employee in setting career paths.
Must Have Skills
R Statistical Package
Python
Billing
Order to Cash Order Management
Good To Have Skills
SQL
SAS
Technical Skills
SNo
Primary Skill
Proficiency Level *
Rqrd./Dsrd.
1
SQL
PL4
Desired
2
R Statistical Package
PL1
Required
3
Python
PL1
Required
4
SAS
PL4
Desired
Domain Skills
SNo
Primary Skill
Proficiency Level *
Rqrd./Dsrd.
1
Billing
NA
Required
2
Order to Cash Order Management
NA
Required
* Proficiency Legends
Proficiency Level
Generic Reference
PL1
The associate has basic awareness and comprehension of the skill and is in the process of acquiring this skill through various channels.
PL2
The associate possesses working knowledge of the skill, and can actively and independently apply this skill in engagements and projects.
PL3
The associate has comprehensive, in-depth and specialized knowledge of the skill. She / he has extensively demonstrated successful application of the skill in engagements or projects.
PL4
The associate can function as a subject matter expert for this skill. The associate is capable of analyzing, evaluating and synthesizing solutions using the skill.
