AI Lead - Mexico City, México - Cognizant

    Cognizant
    Cognizant Mexico City, México

    Encontrado en: Talent MX C2 - hace 1 semana

    cognizant background
    De jornada completa
    Descripción

    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

    • Employee Status : Full Time Employee

    Shift : Day Job

    Travel : No

    Job Posting : Mar