Ai Lead

**Not Applicable**

**Qualification**:

- **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**
- Azure Open AI Service
- AWS Machine Learning
- Deep Learning
- Python

**Good To Have Skills**
- Spark ML
- Statistics
- Transformer
- EDA(Exploratory Data Analysis)
- Google Vertex AI
- D365 Common Data Service
- Google Cloud Natural Language
- Dialogflow Virtual Agents
- Dialogflow Agent Assist
- IBM Watson Natural Language
- Knowledge Graph
- TensorFlow Quantum
- Azure Computer Vision
- ML Ops
- DataRobot
- Rust
- Neuro AI
- Dataiku
- Machine Learning
- Azure Cognitive Search
- Cloud AutoML
- BigQuery ML
- AutoML Tables
- Dialogflow
- Tensorflow Serving
- OpenCV
- Artificial Intelligence
- Amazon Sagemaker
- Databricks
- IoT
- Google Dialogflow