About the job
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 modelling are part of daily activities.
- Involved in development, testing, evaluation, and optimisation of models developed.
- Analyses data and generates insights that can be 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 availability 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 the right data is captured for analysis and modelling.
Project Management
- Ensures that all the deliverables meet 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.
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 the exploration phase.
- Present initial results to business stakeholders and identify the next steps.
Design experiments with test and validate multiple hypotheses to meet/exceed the expectations of customers 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 techniques: Classification, Clustering, Time Series, Market Basket Analysis, Text Mining (Structured and Unstructured Data), NLP, Decision Trees, Network Analysis, Linear Programming, Optimization, 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 thought leadership and dependable execution on diverse projects.
- Implement best practices and technology.
- Discover new avenues by dissecting the data and identify which all models can be utilized for a given business problem.
- Provide expertise thru PoCs and PoVs.
Knowledge Management
- Prepare a design, requirement document.
- Document all modelling steps in a systematic way, including the modelling 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 Associate Data Scientists.
- Set the timelines and monitor the progress of the project.
- Ensure the timely delivery of deliverables and address the concerns related to tasks.
- Understand the aspirations of team members.
- Set goals for team members and monitor performance.
