AI Lead

hace 3 días

Mexico City Cognizant A tiempo completo
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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