AI Lead - (Job Number: )

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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

• Natural Language Processing

• PySpark

• PyTorch

• MATLAB Optimization Toolbox

• Chatbots

• Azure BOT Service

• R Shiny

• Knime

• Julia

• Statistica

• R Studio

• keras

• Tensorflow

• BayesiaLabs

• Octave

• SPSS

• Alteryx

• Azure Machine Learning

• Watson

• Vertex

• Retail - Markdown Optimization

• Apache Hadoop

• R Statistical Package

• MS Excel

• Search Engine Optimization

• MATLAB

• SAS

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Technical Skills

SNo Primary Skill Proficiency Level * Rqrd./Dsrd. 1Spark MLPL3Desired2StatisticsPL3Desired3Azure Open AI ServicePL3Required4TransformerPL3Desired5EDA(Exploratory Data Analysis)PL3Desired6Google Vertex AIPL3Desired7D365 Common Data ServicePL2Desired8Google Cloud Natural LanguagePL3Desired9Dialogflow Virtual AgentsPL3Desired10Dialogflow Agent AssistPL2Desired11IBM Watson Natural LanguagePL2Desired12Knowledge GraphPL2Desired13TensorFlow QuantumPL2Desired14Azure Computer VisionPL3Desired15ML OpsPL2Desired16AWS Machine LearningPL3Required17DataRobotPL3Desired18RustPL2Desired19Neuro AIPL4Desired20DataikuPL3Desired21Machine LearningPL4Desired22Azure Cognitive SearchPL2Desired23Cloud AutoMLPL2Desired24BigQuery MLPL3Desired25AutoML TablesPL3Desired26DialogflowPL2Desired27Tensorflow ServingPL2Desired28OpenCVPL2Desired29Artificial IntelligencePL3Desired30Amazon SagemakerPL3Desired31DatabricksPL2Desired32IoTPL2Desired33Google DialogflowPL2Desired34Natural Language ProcessingPL3Desired35Deep LearningPL2Required36PySparkPL4Desired37PyTorchPL2Desired38MATLAB Optimization ToolboxPL2Desired39ChatbotsPL2Desired40Azure BOT ServicePL2Desired41R ShinyPL2Desired42KnimePL2Desired43JuliaPL3Desired44StatisticaPL2Desired45R StudioPL3Desired46kerasPL3Desired47TensorflowPL2Desired48BayesiaLabsPL3Desired49OctavePL2Desired50SPSSPL3Desired51AlteryxPL2Desired52Azure Machine LearningPL3Desired53WatsonPL4Desired54VertexPL3Desired55Retail - Markdown OptimizationPL2Desired56Apache HadoopPL3Desired57R Statistical PackagePL2Desired58MS ExcelPL2Desired59PythonPL3Required60Search Engine OptimizationPL2Desired61MATLABPL3Desired62SASPL2Desired

* Proficiency Legends

Proficiency Level Generic Reference PL1The associate has basic awareness and comprehension of the skill and is in the process of acquiring this skill through various channels.PL2The associate possesses working knowledge of the skill, and can actively and independently apply this skill in engagements and projects.PL3The 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.PL4The associate can function as a subject matter expert for this skill. The associate is capable of analyzing, evaluating and synthesizing solutions using the skill.