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    Senior Analyst - chennai, India - Ford Motor Company

    Ford Motor Company
    Ford Motor Company chennai, India

    1 week ago

    Default job background
    Full time
    Description
  • Candidates should have significant hands-on experience with analytical projects using AI/ML techniques.
  • Candidates will be expected to successfully prioritize and manage multiple analytical projects / ad-hoc requests
  • Hands-on experience in Predictive and Prescriptive Analytics
  • Good exposure to various techniques like linear/logistic regression models, discrete choice models, time-series models, dimensionality reduction, and unsupervised learning techniques
  • Good technical depth with strong analytical/programing skills and ability to apply technical knowledge to do research.
  • R / SAS / Python/SQL or other quantitative analysis programming skills
  • Should have strong hands-on experience in GitHub and GCP tools such as Big query, vertex AI, data proc, ETL in GCP, deployment and GenAI, artifact registry in GCP.
  • Develop statistical methodologies and deploy analytical tools to support different business initiatives
  • Continual enhancement of statistical techniques and their applications in solving business objectives
  • Compile and analyze the results from modeling output and translate into actionable insights
  • Acquire and share deep knowledge of data utilized by the team and its business partners
  • Collaborate with various stake holders to create/improve data products to meet business objectives.
  • Support and share knowledge with other team members for cross functional collaboration.
  • Evaluate new tools and technologies to improve analytical processes
  • Set own priorities and timelines to accomplish projects (accountability for project deliverables)
  • Need 4 years Bachelor or Masters in mathematics/Statistics/Applied Mathematics/Applied Statistics.
  • 2 to 3 years experience in AL/ML techniques and GCP tools with strong SQL, R and Python knowledge.
  • Good exposure to various techniques like linear/logistic regression models, discrete choice models, time-series models, dimensionality reduction, and unsupervised learning techniques.
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